Final Contaminant Candidate List 3 

Chemicals: Classification of the 

PCCL to CCL 
Office of Water (4607M) 

EPA 815-R-09-008 

August 2009 

www.epa.gov/safewater 
 

 

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EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Table of Contents
 

1.0 INTRODUCTION TO THE CONTAMINANT CANDIDATE LIST (CCL)
 

CLASSIFICATION PROCESS........................................................................................1
 

1.1 Principles of Evaluation .................................................................................................... 2
 

1.2 Developing the Classification Approach.......................................................................... 3
 

2.0 ATTRIBUTES.........................................................................................................................5
 

2.1 Health Effects Attributes................................................................................................... 6
 

2.1.1 Potency.......................................................................................................................... 6
 

2.1.2 Severity ....................................................................................................................... 15
 

2.2 Occurrence Attributes..................................................................................................... 20
 

2.2.1 Prevalence and Magnitude Data Elements.................................................................. 21
 

2.2.2 Prevalence - Calibrating Scales and Scoring .............................................................. 22
 

2.2.3 Evaluation of the Prevalence Protocol........................................................................ 23
 

2.2.4 Magnitude - Calibrating Scales and Scoring .............................................................. 24
 

2.2.5 Persistence-Mobility as a Surrogate Measure for Magnitude..................................... 29
 

2.2.6 Persistence-Mobility Data – Calibrating Scales and Scoring ..................................... 30
 

2.2.7 Evaluation of the Magnitude Protocol ........................................................................ 31
 

2.3 Fine Tuning the Protocols ............................................................................................... 32
 

3.0 DEFINITIONS AND OVERVIEW OF THE TRAINING DATA SET ..........................32
 

3.1 Key Considerations.......................................................................................................... 33
 

3.2 Developing Key Components of the Training Data Set................................................ 33
 

3.2.1 Attribute Scores .......................................................................................................... 33
 

3.2.2 Making List-Not list Decisions................................................................................... 37
 

4.0 PROTOTYPE CLASSIFICATION MODELS AND THE CCL PROCESS..................38
 

4.1 Model Training and Development.................................................................................. 39
 

4.2 Model Sensitivity Analyses.............................................................................................. 41
 

4.2.1 Training with subsets of the TDS ............................................................................... 41
 

4.2.2 Training after Selected “Outliers” Are Removed From the TDS ............................... 42
 

4.2.3 Graphical and Statistical Analyses to Identify Significant Differences in 

Attribute “Weights” Or Influence on Model Performance ....................................... 43
 

4.3 Model Performance Testing............................................................................................ 45
 

4.4 Evaluating Classification Differences ............................................................................ 46
 

4.4.1 Classification Differences Among the Models ........................................................... 47
 

4.4.2 Logical Evaluation of the Models – Graphical Analysis ............................................ 49
 

4.5 Applying Model Results .................................................................................................. 55
 

4.5 Applying Model Results .................................................................................................. 56
 

4.5.1 Additive Model Results .............................................................................................. 56
 

4.5.2 Additive Rank Order Results ...................................................................................... 56
 
 

 

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5.0 MODEL OUTCOME AND POST MODEL EVALUATION PROCESS ......................58
 

5.1 PCCL Characterization and Model Results.................................................................. 58
 

5.2 Evaluation of the Modeling Output ............................................................................... 59
 

5.2.1 Procedure .................................................................................................................... 59
 

5.2.2 Evaluation Results ...................................................................................................... 60
 

5.3 Post-Model Adjustments to Output ............................................................................... 62
 

5.3.1 Using Supplemental Sources to Identify the Data Most Relevant to Drinking
 

Water......................................................................................................................... 63
 

5.3.2 Calculation of a Health-Concentration Ratio for Contaminants with Water
 

Data ........................................................................................................................... 63
 

5.3.3 Grouping Contaminants based on Data Certainty ...................................................... 66
 

5.3.4 LD50 Values with Limited Documentation ................................................................. 67
 

5.4 Selecting the Draft CCL 3............................................................................................... 67
 

5.5 Summary........................................................................................................................... 68
 

6.0 REFERENCES......................................................................................................................69
 

7.0 APPENDICES.......................................................................................................................70
 

Appendix A. Attribute Scoring Protocols........................................................................... A-1
 

Appendix B. Information Sheets from the TDS Exercises.................................................B-1
 

Appendix C. Summary of EPA Team TDS Decisions ....................................................... C-1
 

Appendix D. Software Sources ............................................................................................ D-1
 

Appendix E. Solutions ...........................................................................................................E-1
 

Appendix F. Chemicals Reviewed by the EPA Evaluation Team: Summary of 

Results.........................................................................................................................F-1
 

Appendix G. PCCL Contaminants with Incomplete Data for Scoring or that had 

Parent Compounds Scored in Developing the Draft CCL 3.................................G-1
 

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Classification of the PCCL to CCL August 2009 

Table of Exhibits
 

Exhibit 1. Developing an Approach to Process PCCL Chemicals ................................................. 4
 

Exhibit 2. Decile Distribution of RfD Values (mg/kg/day)............................................................ 8
 

Exhibit 3A. Logarithmic Distribution of RfD Values .................................................................... 9
 

Exhibit 3B. Logarithmic Distribution of NOAEL Values............................................................. 9
 

Exhibit 3C. Logarithmic Distribution of LOAEL Values ............................................................ 10
 

Exhibit 3D. Logarithmic Distribution of LD50 Values ................................................................. 10
 

Exhibit 4. Scoring Equations for Potency..................................................................................... 12
 

Exhibit 5. Logarithmic Distribution of Cancer Potency Values................................................... 13
 

Exhibit 6. Potency Scores for Chemicals in the Learning Set ...................................................... 14
 

Exhibit 7. Potency Scores for Chemicals Not in the Learning Set ............................................... 14
 

Exhibit 8. NRC Severity Scoring Proposal................................................................................... 16
 

Exhibit 9. Final Nine-Point Scoring Protocol for Severity........................................................... 17
 

Exhibit 10. Relationship of Data Elements Used to Score Magnitude and Prevalence................ 21
 

Exhibit 11. Comparison of Prevalence Scores for Learning Set Contaminants ........................... 23
 

Exhibit 12. Comparison of the NRC Magnitude Score with the Ratio of the Health 

Advisory Guideline to the Concentration in Finished Water....................................... 25
 

Exhibit 13. Magnitude Concentrations and Scores Derived from Potency Doses ....................... 26
 

Exhibit 14A. Equal Bins Drinking Water Magnitude Scale (ug/L).............................................. 27
 

Exhibit 14B. Half Log Option A Drinking Water Magnitude Scale (ug/L) ................................. 27
 

Exhibit 14C. Half Log Option B Drinking Water Magnitude Scale (ug/L) ................................. 28
 

Exhibit 15. Magnitude Attribute Scores: Example Contaminants Scored by their Median 

of Detections Using the Various Approaches in Exhibit 14. ....................................... 28
 

Exhibit 16. Mobility and Persistence Data Elements.................................................................... 29
 

Exhibit 17. Comparison of Scores derived using the Magnitude Protocol................................... 31
 

Exhibit 18. Combinations of low and high attribute scores1 for the four attributes using
 

Latin Hypercube Sampling. ......................................................................................... 35
 

Exhibit 19. Attribute Space for the 101 TDS compared to that for the 202 TDS......................... 36
 

Exhibit 20a. QUEST Classifications Based on the Full Training Data Set.................................. 40
 

Exhibit 20b. QUEST Classifications Based on 5-Fold Cross-Validation..................................... 40
 

Exhibit 21. Linear Model-estimated versus Team Average Classification for the TDS ............. 43
 

Exhibit 22. Relative Weights of Attributes at QUEST Nodes...................................................... 44
 

Exhibit 23. Summary Statistics from MCMC Sample.................................................................. 45
 

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Classification of the PCCL to CCL August 2009 

Exhibit 24. Features of the Three Preferred Models Based on TDS Test Results........................ 46
 

Exhibit 25. Decision Comparison Matrix; Weight of Differences ............................................... 47
 

Exhibit 26. Summary of Quaternary Model Decisions................................................................. 48
 

Exhibit 27. Results of 202 Model Classifications and Weighted Misclassifications ................... 48
 

Exhibit 28. Summary of Individual Quaternary Model Classifications ....................................... 50
 

Exhibit 29. ANN Model Predictions for the Four Attribute Space............................................... 51
 

Exhibit 30. MARS Model Predictions for the Four Attribute Space............................................ 53
 

Exhibit 31. Univariate CART Model Predictions for the Four Attribute Space........................... 54
 

Exhibit 32. Linear Model Predictions for the Four Attribute Space............................................. 54
 

Exhibit 32. Linear Model Predictions for the Four Attribute Space............................................. 55
 

Exhibit 33. Summary Comparison of the Sum of the 3 Model Decisions to the
 

Distribution of EPA Blinded (TDS) Decisions............................................................ 57
 

Exhibit 34. Model Results for the PCCL Chemicals .................................................................... 58
 

Exhibit 35. Results of the Model Output Evaluation (Total = 129 chemicals)............................. 62
 

Exhibit 36. Formulae used in the CCL 3 Process to Calculate Health Reference Levels
 

(HRLs) from the CCL 3 Potency Data Elements......................................................... 64
 

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List of Abbreviations and Acronyms
 

< Less than 

< Less than or equal to 

> Greater than 

> Greater than or equal to 

μg	 Microgram, one-millionth of a gram 

μg/L	 Micrograms per liter 

ANN	 Artificial Neural Network 

ATSDR	 Agency for Toxic Substances and Disease Registry 

CART	 Classification and Regression Tree 

CASRN Chemical abstract services registry number 

CCL	 Contaminant Candidate List 

CCL 1 EPA’s first contaminant candidate list 

CCL 2 EPA’s second Contaminant Candidate List 

CCL 3	 EPA’s third Contaminant Candidate List 

CUS/IUR	 Chemical update system/inventory update rule 

DBP	 Disinfection byproduct 

EDWC	 Estimated Drinking Water Concentration 

EEC	 Estimated Environmental Concentration 

EPA United States Environmental Protection Agency 

g/day Grams per day 

HRL Health Reference Level 

IOC	 Inorganic compound 

IRIS	 Integrated Risk Information System 

kg	 Kilogram 

L	 Liter 

LD50	 Lethal dose 50; an estimate of a single dose that is expected to cause the death 

of 50 percent of the exposed animals; it is derived from experimental data. 

lbs	 Pounds 

LOAEL Lowest observed adverse effect level 

MARS	 Multivariate Adaptive Regression Splines 

MCMC	 Markov Chain Monte Carlo 

mg Milligram, one-thousandth of a gram 

mg/kg Milligrams per kilogram body weight 
 

 

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mg/kg/day Milligrams per kilogram body weight per day 

mg/L Milligrams per liter 

N Number of samples 

NAWQA National water quality assessment (USGS program) 

NCOD National contaminant occurrence database 

ND Not detected (or non-detect) 

NDWAC National Drinking Water Advisory Council 

NIRS National Inorganic and Radionuclide Survey 

NOAEL No observed adverse effect level 

NRC National Research Council 

OW Office of Water 

OPP Office of Pesticide Programs 

PBPK Physiologically Based Pharmacokinetic 

PCCL Preliminary-CCL 

PWS Public water system 

QUEST Quick, Unbiased, Efficient Statistical Tree 

RTECs Registry of Toxic Effects of Chemical Substances 

RfD Reference dose 

TDS Training data set 

TRI Toxics Release Inventory 

UCMR Unregulated Contaminant Monitoring Regulations 

UCMR 1 First Unregulated Contaminant Monitoring Regulation 

UCMR 2 Second Unregulated Contaminant Monitoring Regulation 

UL Tolerable upper intake level 

US United States of America 

USGS United States Geological Survey 
 

 

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1.0 INTRODUCTION TO THE CONTAMINANT CANDIDATE LIST 

(CCL) CLASSIFICATION PROCESS 

Every five years the United States Environmental Protection Agency (EPA) is required to 

publish a list of contaminants (1) that are currently unregulated, (2) that are known or anticipated 

to occur in public water systems, and (3) which may require regulations under the Safe Drinking 

Water Act (SDWA). This list is known as the Contaminant Candidate List or CCL. SDWA 

section 1412(b)(1) requires that in the development of the CCL, EPA consider specific data 

sources and include the scientific community. EPA must evaluate substances identified in section 

101(14) of the Comprehensive Environmental Response, Compensation, and Liability Act 

(CERCLA) of 1980 and substances registered as pesticides under the Federal Insecticide, 

Fungicide, and Rodenticide Act (FIFRA). SDWA also requires the Agency to consider the 

National Contaminant Occurrence Database established under section 1445(g) of SDWA. 

SDWA directs the Agency to consult with the scientific community, including the Science 

Advisory Board (SAB). In addition, it directs the Agency to consider the health effects and 

occurrence information for unregulated contaminants to identify those contaminants that present 

the greatest public health concern related to exposure from drinking water. 

 

EPA interprets the criterion that contaminants are known or anticipated to occur in public water 

systems broadly. In evaluating this criterion, EPA considers not only public water system 

monitoring data, but also data on concentrations in ambient surface and ground waters, releases 

to the environment (e.g., Toxics Release Inventory), and production. While such data may not 

establish conclusively that contaminants are known to occur in public water systems, EPA 

believes these data are sufficient to anticipate that contaminants may occur in public water 

systems and support their inclusion on the CCL. The Agency considered adverse health effects 

that may pose a greater risk to life stages and other sensitive groups which represent a 

meaningful portion of the population. Adverse health effects associated with infants, children, 

pregnant women, the elderly, and individuals with a history of serious illness were evaluated. In 

selecting contaminants for the CCL 3, each of the above requirements was met. 

SDWA section 1412(b)(1) also requires EPA to determine whether to regulate at least five 

contaminants from the CCL every five years. SDWA specifies that EPA shall regulate a 

contaminant if the Administrator determines that: 

•	 The contaminant may have an adverse effect on the health of persons; 

•	 The contaminant is known to occur, or there is a substantial likelihood that the 

contaminant will occur in public water systems with a frequency and at levels of public 

health concern; and 

•	 In the sole judgment of the Administrator, regulation of such contaminant presents a 

meaningful opportunity for health risk reduction for persons served by public water 

systems. 

Once contaminants have been placed on the CCL, EPA identifies if there are any additional data 

needs or if there are sufficient information to make a regulatory determination. EPA interprets 

these criteria for regulatory determination as more rigorous than what is used to place 

contaminants on the CCL. 
 

 

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EPA developed a multi-step approach to select contaminants for the third CCL (CCL 3), which 

includes the following key steps: 

(1)	 The identification of a broad universe of potential drinking water contaminants 

(CCL 3 Universe); 

(2)	 A screening process that uses straightforward screening criteria, based on a 

contaminant’s potential to occur in public water systems and thereby pose a 

potential public health concern, to narrow the universe of contaminants to a 

Preliminary-CCL (PCCL); and 

(3)	 A structured classification process (e.g., a prototype classification algorithm 

model) that objectively compares data and information as a tool and is evaluated 

along with expert judgment to develop a CCL from the PCCL. 

Steps 1 and 2 in the process are described in other support documents: Final CCL 3 Chemicals: 

Identifying the Universe (EPA, 2009a); and Final CCL 3 Chemicals: Screening to a PCCL (EPA, 

2009b). The purpose of this document is to describe the methodology used to develop the 

classification process (Step 3) and the process used to select chemicals for the CCL 3. 

The PCCL consisted of 561 chemicals that were screened from the CCL3 Universe. To select 

contaminants for the CCL 3, EPA used classification models to handle larger, more complex 

assortments of data in a consistent and reproducible manner. Learning from EPA’s experience 

and expertise, the classification models were trained based on past expert decisions. The 

algorithms were used to prioritize chemicals which allowed the final expert evaluation and 

review to be more objective and efficient. The data and information used to evaluate 

contaminants on the PCCL is provided in Contaminant Information Sheets available in the 

CCL 3 docket (EPA-HQ-OW-2007-1189) at www.regulations.gov. 

1.1 Principles of Evaluation 

In developing the first CCL (CCL 1), the Agency utilized readily available occurrence and health 

effects information coupled with an expert review process. Following the publication of CCL 1, 

the Agency sought the advice of the National Research Council (NRC) and National Drinking 

Water Advisory Council (NDWAC). The panels provided recommendations to guide EPA in 

creating a more comprehensive and transparent evaluation of potential drinking water 

contaminants for developing future CCLs. In the light of the NRC and NDWAC 

recommendations, EPA has reviewed and evaluated a large number of contaminants and their 

data, developed decision making protocols using classification algorithm approaches, and 

included expert review in arriving at decisions to list or not list contaminants on CCL 3. These 

steps have provided a decision process that is more transparent and reproducible than approaches 

used for previous CCLs. The process is driven by the data on individual contaminants and 

minimizes the bias that may occur with expert panels related to the participants’ individual 

backgrounds and the effects of group dynamics. As experience is gained, the new classification 

process is likely to evolve and improve for application to future CCLs. 
 

 

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To guide the development of the classification process, EPA identified several key features that 

the approach addresses. 

1.	 Meaningful Basis for Classification. The classification process must reflect the critical 

goals of the CCL; that is, it must consider the potential for occurrence in water, the 

potential for causing adverse health effects, and it must prioritize contaminants based on 

these criteria. The data supporting the list no-list decision must be linked back to these 

three tenets. 

2.	 Incorporating Relevant Data. The most relevant data used for the classification process 

include health effects data that are appropriate for drinking water exposures, and 

occurrence data that indicate the nature and spatial extent of potential occurrence in 

drinking water. 

3.	 Transparent Process for Communication. One goal of the classification approach is to 

provide a transparent process that can be reviewed by external experts and the public. 

The attributes and data characterizing the contaminants should be easy to understand and 

the decision-making process to list or not list a particular contaminant must be conveyed 

in a straight forward manner. 

4.	 Reproducibility. A key feature of the classification process is that it should be 

reproducible. The classification process should always give the same result for the same 

set of input information. 

1.2 Developing the Classification Approach 

Based on this framework, EPA developed an approach for classifying potential drinking water 

contaminants. An overarching premise in using classification models to prioritize contaminants 

is that different contaminants can be compared on the basis of similar attributes. The approach 

ensures that the contaminant attributes reflect the key decision characteristics in deciding 

whether or not to list a contaminant on the CCL. The attributes are properties used to categorize 

contaminants for their potential to occur in drinking water and for their potential to cause adverse 

health effects. For example, occurrence can be characterized by a contaminant’s water 

concentration data or potential to occur based on its release to the environment. The adverse 

health effects of contaminants can be characterized using preliminary toxicological data such as 

median lethal dose (LD50) or more developed values such as oral reference doses (RfDs). To 

evaluate, categorize, and prioritize the PCCL contaminants as potential CCL contaminants, EPA 

integrated various types of data that represent measures of their attributes. This relative 

assessment across data measures normalized the available data by developing a set of attribute 

scales for the attribute data, and scoring mechanisms for the various types of data available for 

potential drinking water contaminants. 

Because of this new approach and its new application, EPA developed, tested, and evaluated the 

results of several classification algorithms to assess whether they are useful, and which ones 

might provide the best decision support tools. To test and evaluate the process, EPA developed a 

data set and used it to “train” the classification algorithms. Once the modeling was completed, 

EPA evaluated the model output based on the compilation of data for a subset of the modeled 
 

 

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contaminants and assisted in developing a process to utilize the model output to generate the 

CCL 3. The following chapters describe the steps EPA used to develop the components of the 

classification process, as displayed in Exhibit 1. 

 

 

Exhibit 1. Developing an Approach to Process PCCL Chemicals

Develop Attribute 

Scoring Protocol 

Select Training Data Set 

Contaminants and Make 

Listing Decisions 

Score Training Data Set 

Contaminants with Final Attribute 

Scoring Protocols 

Train and Validate Classification 

Approaches using Training Data Set 

Iterative Process – 

The results of training 

and validation will 

indicate if areas need 

further evaluation and 

refinement. The iterative 

process may or may not 

go back to the primary 

assumptions. 

Adequate 

Results 

Apply to 

PCCL 

Post-model 

evaluation of PCCL 

chemicals 

Yes 

No 

Chapter 2 describes the attributes and scoring protocols. Chapter 3 describes the set of chemicals 

used to train the classification models, the training data set. Chapter 4 describes how the models 

were calibrated using the attributes and training data set. Chapter 5 describes the evaluation of 

the model output and post model processes. 
 

 

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2.0 ATTRIBUTES 

Attributes are used to characterize different chemicals on the basis of similar qualities or traits. 

These qualities or traits represent the anticipated occurrence or adverse health effects of each 

contaminant. Occurrence and health effects are both represented by different types of data. To 

evaluate contaminants as potential CCL contaminants, one must be able to establish consistent 

relationships among the different types of data that represent measures of the attributes. This 

process involves the need to normalize the available data by developing scales and scoring 

mechanisms that will accept a variety of input data. The attributes are properties used to 

categorize contaminants for their potential to occur in drinking water and for their potential to 

cause adverse health effects. For example, occurrence may be characterized by water 

concentration data or a contaminant’s potential to occur based on its release to the environment. 

The adverse health effects of contaminants may be characterized using preliminary toxicological 

data such as median lethal dose (LD50) or more developed values such as oral reference doses. 

The NRC recommended using the attributes Potency and Severity to describe health effects, and 

Prevalence and Magnitude to describe occurrence. When occurrence data are not available, they 

also suggested that environmental fate properties (i.e. Persistence and Mobility) could be used as 

surrogates to estimate potential for occurrence. EPA agreed that the recommended attributes are 

appropriate and consistent with data used in the past decision-making efforts by EPA’s Office of 

Water (OW). 

Throughout the process of evaluating the attributes, it was recognized that a wide range of data 

elements would have to be used to characterize each attribute. The CCL process involves 

classifying relatively new and emerging contaminants and most will not have complete dossiers 

of data. If the same data were available for all chemicals their comparison and prioritization 

would be relatively straight forward. However, the types of data available for unregulated 

chemical contaminants varies. To enable comparisons among chemicals with differing types of 

data and information, a scaling system that accepts a variety of input data, yet provides a 

consistent comparative framework, is needed. In concert with NRC and NDWAC 

recommendations, EPA identified the following principles to guide development of the attribute 

scoring process: 

•	 Attribute scores should increase with concern (e.g., a 10 is of greater concern, 1 of lesser 

concern); 

•	 There should be sufficient scoring categories to capture the range of data and to 

discriminate among the data;
 

•	 The number of categories should not be so great that they create a false sense of
 

precision;
 

•	 Attributes can use different numbers of scoring categories if necessary (i.e., Prevalence 

could use 1-10, while Severity could use 1-8); 

•	 The possible range of the scores for a given attribute should be the same regardless of the 

data elements that are used to assign the score for that attribute; 

•	 The data source and data element used for each attribute should consider more direct 

measures of occurrence or health effects before potential measures; peer reviewed data 

before unpublished data, and measured data before modeled data. 
 

 

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•	 The calibration scale (i.e., the scale relating the range for a data element to the scoring 

categories) should be established using a representative “universe” of data for each 

attribute to capture the potential range of values that might be encountered; 

•	 The calibration scale must be set and remain constant throughout the operational process; 

and 

•	 The scoring approach should be as simple as possible and data should be used with 

minimal transformations. 

Section 2.1 describes the development of the process used to score the health effects attributes, 

and section 2.2, the approach for the occurrence attributes. 

2.1 Health Effects Attributes 

Potency and Severity are the two attributes used for evaluating health effects. As defined in 

detail below, Potency reflects the lowest dose of a chemical that causes an adverse health effect 

in a case study report or in a toxicological or epidemiological study. Severity is the adverse 

health effect associated with the dose that is used as the measure of Potency, and is calibrated 

based on the health-related significance of the adverse effect (e.g., dermatitis versus cancer). 

These two attributes are interrelated, in that the Severity is linked to the measure of Potency. 

2.1.1 Potency 

Potency is a value that indicates the power of a contaminant to cause adverse health effects. In 

the case of chemicals, that power is apparent in the dose required to cause the most sensitive 

manifestation of an adverse health effect, or to generate a particular excess cancer risk. Potency 

for chemicals is reflected in several standard toxicological parameters that are discussed below. 

A number of approaches have the potential to be useful in scoring the Potency attribute. 

However, regardless of the approach selected, the methods require calibrating the scores to 

normalize the scale. To evaluate the data elements and establish consistent scales, an initial 

“learning set” of about two hundred chemicals was developed for use in experimentation with 

approaches to calibration. The chemicals considered included regulated chemicals and 

unregulated chemicals for which EPA has derived Health Advisories (EPA, 2004). These 

chemicals are primarily at the high end of the Potency scale. To ensure that the Potency scale 

covers the full range of conditions that may be encountered (from high to low Potency) in a 

universe of chemicals, a group of chemicals (nutrients/food additives) that are generally 

considered as relatively non-toxic and have toxicity values that can be compared to health 

advisories were added to the learning set. 
 

 

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The following toxicity parameters were compiled for the learning set chemicals, and their 

numeric distribution across the range of values was examined (see the footnotes below for 

definitions of the terms). 

•	 Reference Dose (RfD)

1 

or equivalent 

•	 Cancer potency 

2 (concentration in water equivalent to a 10-4 

cancer risk) 

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•	 No Observed Adverse Effect Level (NOAEL) 3 and/or Lowest Observed Adverse Effect 

Level (LOAEL)4 associated with the RfD 

•	 Rat oral median Lethal Dose (LD5 )

5 

0 

Several approaches to characterize the distribution of values for the different toxicity parameters 

were employed in this exercise. The approaches are described in the following section. 

The data for the learning set were obtained from the following sources: 

•	 EPA’s Integrated Risk Information System (IRIS) 

•	 EPA’s Office of Water Health Advisories Documents

6 

•	 Registry of Toxic Effects of Chemical Substances (RTECS) (Mostly LD50 values) 

•	 Tolerable Upper Intake Levels (ULs) from the Institute of Medicine Dietary Reference 

Intakes 

1 

 A Reference Dose (RfD) is an estimate (with uncertainty spanning perhaps an order of 

magnitude) of a daily exposure to the human population (including sensitive subgroups) that is likely to 

be without an appreciable risk of deleterious effects during a lifetime. It is expressed in mg/kg/day. The 

Agency for Toxic Substances and Disease Registry (ATSDR) lifetime Minimal Risk Levels (MRLs), 

World Health Organization (WHO) Tolerable Daily Intakes (TDIs), WHO and Food and Drug 

Administration (FDA) Acceptable Daily Intakes (ADIs), and the Institute of Medicine (IOM) nutrient 

Tolerable Upper Intake Levels (ULs) are roughly equivalent to the RfD. 

2 

 For this exercise cancer potency was evaluated as the concentration in drinking water 

equivalent to an excess cancer risk of one case in 10,000 (10-4). This value is given in the Office of Water 

(OW) Drinking Water Standards and Health Advisories Tables and also is included in all Integrated Risk 

Information System (IRIS) Summary documents. When the 10-4 risk value is not available, it can be 

calculated from a cancer slope factor. 

3 

 NOAEL is a No-Observed-Adverse-Effect Level. It is the highest dose in a toxicological study 

or a group of studies that has no observed adverse effect. 

4 

 LOAEL is a Lowest-Observed-Adverse-Effect Level. It is the lowest dose in a toxicological 

study or a group of studies that causes an adverse health effect. 

5 

 An oral median Lethal Dose (LD50) is an estimate of the oral dose that will cause the death of 

50 percent of the exposed animals. LD50 data are based on acute exposures with limited post-exposure 

observations of the animals for cause of mortality, clinical signs, and gross pathology. 

6 

 The 2002 Edition of the Drinking Water Standards and Health Advisories was used for the 

RfD and 10-4 risk values. 
 

 

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Exhibit 2. Decile Distribution of RfD Values (mg/kg/day)

160 

140 

120 

100 

80 

60 

40 

20 

0 

Frequency 

0 <=0.1 >0.1-0.2 >0.2-0.3 >0.3-0.4 >0.4-0.5 >0.5-0.6 >0.6-0.7 >0.7-0.8 >0.8-0.9 >0.9 

RfD 

(mg/kg/day) 

2.1.1.1 Potency Data – Calibrating Scales and Scoring 

Once the data for the learning set of chemicals was collected, they were arrayed and graphically 

displayed to analyze their range and distribution. For the initial evaluation, the range (in 

mg/kg/day) was divided into approximately ten equal units (deciles). This distribution was found 

to be highly skewed, with a large majority of the values falling in the decile of highest toxicity 

(see Exhibit 2 for an example). Two factors influenced this result. The first factor is that the 

range of values covered up to twelve orders of magnitude for the parameters evaluated. The 

second factor is that the set of contaminants contained both toxic chemicals as well as those 

generally regarded as safe (in keeping with the principles) and there are far more toxicological 

data available in the literature on chemicals considered to be toxic than for those, like the 

nutrients, that are only weakly toxic. This shifts the volume of data toward the chemicals with 

higher potencies. Most chemicals that are generally regarded as safe have limited available 

toxicological data, as their nutritional and commercial uses do not indicate a potential hazard at 

low to moderate intakes. 

The second distribution evaluated was based on logarithms (base 10) of the toxicity parameters 

rounded to the nearest integer (see Exhibit 3A-D as examples). 
 

 

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Exhibit 3A. Logarithmic Distribution of RfD Values

80
 

70
 

60
 

50
 

<= -6 -5 -4 -3 -2 -1 0 1 2 3 4 More
 

Round(Log10(RfD))
 

Frequency 40
 

30
 

20
 

10
 

0 

Exhibit 3B. Logarithmic Distribution of NOAEL Values

0 

5 

10 

15 

20 

25 

30 

35 

40 

45 

-5 -4 -3 -2 -1 0 1 2 3 4 5 More 

Round(Log10(NOAEL)) 

Frequency 
 

 

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Exhibit 3C. Logarithmic Distribution of LOAEL Values

0 

5 

10 

15 

20 

25 

30 

35 

40 

45 

<= -5 -4 -3 -2 -1 0 1 2 3 4 5 More 

Round(Log10(LOAEL)) 

Frequency 

Exhibit 3D. Logarithmic Distribution of LD50 Values 

0 

10 

20 

30 

40 

50 

60 

70 

80 

-2 -1 0 1 2 3 4 5 6 7 8 More 

Round(Log10(LD50)) 

Frequency 
 

 

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The decile distribution (Exhibit 2) was found to be undesirable in developing a protocol for 

scoring Potency because almost all of the chemicals are clustered at one end of the distribution. 

This does not provide a good distribution of scores for discrimination of differences. With the 

decile distribution, almost all of the chemicals in the learning set would have a high Potency 

score of 10. Very few chemicals would have lower scores. The distribution based on the rounded 

Log10 of the toxicity parameter provided a distribution that spread the chemical toxicity 

parameters across the range and the most frequent Log10 value is approximately in the middle of 

the range making the curve roughly log-normal Exhibit 3A-3D). It was for this reason that the 

Log10 distribution was selected for development of the scoring equation. The distribution of 

toxicity values is still somewhat skewed toward higher toxicity scores; however, this is a product 

of limited available data for the weakly toxic chemicals. 

The log-based distribution was used to establish a scoring equation for Potency for each measure 

of toxicity. This was accomplished by assigning the most frequent (modal) value in the 

distribution a score of 5 on a 10 point scale and solving an equation for each type of toxicity 

parameter that would make that distributional value equal a score of 5. For example, in Exhibit 

3A (RfD values), the most frequent value is a rounded logarithm of -2 (0.01). The scoring 

equation for the RfD values was developed as follows: 

5 = 10 - (most frequent rounded log + X)
 

5 = 10 - (-2 + X)
 

5 = 10 + 2 -X
 

5 = 12 - X
 

5 - 12 = -X
 

-7 = - X
 

7 = X
 

Accordingly the equation for scoring the RfD values is 

Score = 10 - (rounded log of RfD + 7) 

The scoring equations for the other measures of toxicity were derived from the modal rounded 

logarithm values of their distributions in a similar fashion. As displayed in Exhibit 3, the position 

of the modal rounded log differed for each of the measures of toxicity, and necessitated differing 

equations for each measure. These equations are summarized in Exhibit 4. 
 

 

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Exhibit 4. Scoring Equations for Potency 

RfD Score = 10 - (Log10 of RfD + 7) 

NOAEL Score = 10 - (Log10 of NOAEL + 4) 

LOAEL Score = 10 - (Log10 of LOAEL + 4) 

LD50 Score = 10 - (Log10 of LD50 + 2) 

10-4 cancer risk 1 Score = 10 - (Log10 of the 10-4 cancer risk + 6) 

1. The concentration in water for 10-4 cancer risk in water was selected as the measure of potency 

for carcinogens because this is the value given in the Standards and Drinking Water Health 

Advisories Tables prepared by OW and also is provided in IRIS Summaries. Changing the 

reference value to the 10-6 risk would merely shift the rounded log value and the constant by two 

integers but would not change the score. 

Scores were restricted to whole number values with a maximum of 10 and a minimum of 1. 

Some distributions for toxicity parameters span a range greater than ten orders of magnitude. 

EPA decided that calculated scores less than 1 would be given scores of 1 and calculated scores 

greater than 10 would be given scores of 10, which combine the chemicals at the tails of the 

distributions. Conversely, for the distributions that covered less than 10 orders of magnitude, no 

attempt was made to normalize the scores across a range of ten because the learning set is limited 

and could have been expanded by searching for chemicals that are more toxic than the most toxic 

substance in the learning set (dioxin with an RfD of 1 x 10-9 mg/kg/day) and less toxic than the 

least toxic chemical in the learning set (phosphorous with an RfD-equivalent of 57 mg/kg/day 

derived from the Institute of Medicine (IOM) UL. However an adjustment was made to 

accommodate LD50 values that are reported as greater than a specific numerical dose. In such a 

case, the highest dose used in the study did not cause death in 50 percent of the tested animals, 

indicating that the chemical is less toxic than would be indicated by the highest dose tested. 

Accordingly, the LD50 equation was modified to accommodate this situation and became: 

LD50 Score = 10 - (Log10 of >LD50 + 3) 

This change to the LD50 equation decreases the Potency score from that derived from the numeric 

value of the LD50 by one to accommodate the “greater than” designation. A similar adjustment 

was made for situations where the NOAEL in a critical study was the highest dose tested. 

The distribution for cancer effects is the most skewed of those examined (see Exhibit 5). There 

are a greater number of chemicals that are more potent carcinogens when compared to those in 

the modal grouping than there are those that are less potent. This is not unusual because cancer 

bioassays are costly and there is an incentive to invest resources in studying chemicals that have 

a high likelihood of being potent carcinogens. No attempt was made to further normalize the 

cancer scores across a range of 10. For the chemicals in the learning set, the lowest cancer 

Potency score is 3. 
 

 

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Exhibit 5. Logarithmic Distribution of Cancer Potency Values

0 

5 

10 

15 

20 

25 

<=-6 -5 -4 -3 -2 -1 0 1 2 3 4 More 

Round(Log10(E-4)) 

Frequency 

2.1.1.2 Evaluation of the Potency Scoring Protocol 

All of the chemicals in the learning set were scored for each toxicity parameter to examine the 

consistency across scores for the non-cancer measures of Potency. Some examples of this 

evaluation are provided in Exhibit 6. Since the mechanisms that lead to the development of 

cancer involve some biological responses that are unique to tumors, the 10-4 

cancer risk values 

were not included in this comparison. The scores for individual chemicals were compared across 

the toxicity values, and the agreement between scores was evaluated. 
 

 

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Exhibit 6. Potency Scores for Chemicals in the Learning Set 

Chemical RfD NOAEL LOAEL LD50 

Calcium (Calcium chloride for LD50) 1 ND 4 5 

Cyanazine 6 6 6 6 

Dioxin (2,3,7,8-TCDD) 10 ND 10 4 

Hexazinone 4 5 4 5 

Iodine (Sodium iodide for LD50) 5 8 8 4 

Methyl ethyl ketone 3 3 3 5 

Methyl parathion 7 8 7 7 

Naphthalene 5 4 4 5 

Phenol 4 4 4 5 

Vitamin D 6 9 9 ND 

ND = No data 

In addition, the scoring equations were applied to selected chemicals that were not in the learning 

set using data available in the Agency of Toxic Substances and Disease Registry (ATSDR) 

Toxicological Profiles. Those results are summarized in Exhibit 7. The scores were evaluated for 

consistency across parameters. 

Exhibit 7. Potency Scores for Chemicals Not in the Learning Set 

Chemical/ 

Potency Scores 

RfD-equivalent 

(mg/kg/day) 

NOAEL 

(mg/kg/day) 

LOAEL 

(mg/kg/day) 

LD50 

(mg/kg) 

Acrylonitrile 4 5 5 6 

Ethion 6 7 6 6 

Malathion 5 6 5 5 

Endosulfan 6 7 ND 5 

ND = No Data 

The agreement of non-cancer scores across the RfD, NOAEL, LOAEL and LD50 inputs was 

evaluated. There were 216 chemicals in the learning set; 13.5 percent of those with multiple noncancer scores had identical scores across all parameters (see cyanazine in Exhibit 6). For 54.6 

percent, the scores deviated by 1 integer (see hexazinone in Exhibit 6); 20.5 percent deviated by 

2 integers (see methyl ethyl ketone in Exhibit 6). There was a 3-integer deviation for only 9.7 

percent, and the majority of those were inorganic compounds (see iodine [sodium iodide] in 

Exhibit 6). Only 1.6 percent deviated by more than 3 integers (see dioxin in Exhibit 6). Scores 

deviated by two integers or less for 88.6 percent of the chemicals. The difference between scores 
 

 

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for a given compound was greatest for the relatively non-toxic chemicals. In almost all cases the 

NOAEL and LOAEL scores were higher than the RfD score, effectively negating the concerns 

that the inclusion of uncertainty factors in the calculation of the RfD would inflate the Potency 

score. For those chemicals with low uncertainty factors the NOAEL or LOAEL scores were 

often 3 or more integers higher than the RfD scores (see calcium chloride and vitamin D in 

Exhibit 6). 

Since most chemicals with RfD values are also likely to have NOAEL, LOAEL, and/or LD50 

values, a policy decision was needed with regard to how one should select the parameter used to 

score for a non-cancer endpoint. Since there is a general consistency among scores, EPA 

determined that a hierarchy of RfD> NOAEL> LOAEL> LD50 would be used. In cases where a 

NOAEL is higher than the lowest LOAEL, the LOAEL would be used in its place. This 

hierarchy gives preference to the Potency value with the richest supporting data set (the RfD or 

equivalent values) and the lowest ranking to the LD50 because it is a measure of acute rather than 

chronic toxicity. When comparing cancer and non-cancer scores, it was determined that the end 

point (cancer or non-cancer) that provided the highest measure of Potency would be used to 

score the candidate. 

Similar to the screening protocols, EPA applied the potency scoring protocol for LOAELs to 

contaminants with MRDDs. The Agency did conduct additional searches to identify the best 

available information to characterize the potency and severity for chemicals on the PCCL, 

including pharmaceuticals. If additional information was not found, the Agency relied on the 

data used in the screening step. 

These evaluations were used to develop the scales and hierarchy of data used in the Potency 

Scoring Protocol, which is presented in Appendix A. 

2.1.2 Severity 

Severity refers to the relative impact of an adverse physiological change caused by a xenobiotic 

chemical in humans or animals on the ability of the human or animal to function and survive in 

the environment. The sixteenth century physician, Paracelsus, provided the underlying principle 

for the toxicological sciences with the axiom “the dose makes the poison.” Just as toxicity 

increases with dose, so too does the Severity of the observed effect, in most cases. A low dose 

effect could be a simple increase in liver weight while the same chemical at a higher dose could 

cause cirrhosis of the liver. For that reason, the measure of Severity that will be used for scoring 

in the CCL process is the effect or effects seen at the LOAEL. Restricting Severity scores to the 

effects occurring at the LOAEL ties them to the data used to derive the Potency score – the type 

of data likely to be available for CCL candidates. This approach is consistent with the advice 

provided by the NRC and NDWAC (NRC 2001, NDWAC 2004). 

The Severity measures that will be used for CCL scoring differ from those used for Potency, 

Prevalence, and Magnitude because they are descriptive rather than quantitative. Accordingly, 

they are less amenable to automation and often require more scientific judgment in their 

application. The sections that follow describe the approach that was used to derive the scoring 

protocol for Severity and to evaluate its performance. 
 

 

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Exhibit 8. NRC Severity Scoring Proposal 

Score Description 

0 No effect 

1 Changes in organ weights with minimal clinical significance 

2 Biochemical changes with minimal clinical significance 

3 Pathology of minimum clinical importance (e.g., fluorosis, warts, common cold) 

4 Cellular changes that could lead to disease; minimum functional change 

5 Significant functional changes that are reversible (e.g., diarrhea) 

6 Irreversible changes; treatable disease 

7 Single organ system pathology and function loss 

8 Multiple organ system pathology and function loss 

9 Disease likely leading to death 

10 Death 

2.1.2.1 Severity – Scales and Scoring 

In developing the protocol for scoring Severity, EPA began with the system used by the NRC 

(2001) for their case study on methods for selecting a CCL from a PCCL. The NRC Severity 

scoring protocol was based on the anticipated clinical impact of the most sensitive endpoint in 

affected individuals. The NRC prototype for scoring Severity is provided in Exhibit 8. 

In trying to apply the NRC Severity prototype using the critical effects from EPA IRIS Health 

Risk Assessments, EPA toxicologists encountered difficulty because of the clinical components 

of the prototype. It was difficult to determine clinical outcomes such as function loss, treatability, 

or potential for mortality from the critical effects identified in IRIS. In addition, some of the 

features of a clinical progression could be influenced by the availability and affordability of 

treatment. EPA decided that it would not be appropriate to use a scoring scheme that had 

economic and environmental justice implications. 

The critical effect data for PCCL contaminants will, in most cases, be expressed using 

terminology very similar to the terminology found in the IRIS database. Accordingly, critical 

effects of 100 IRIS chemicals were compiled and grouped into categories by EPA toxicologists. 

These categories were, in turn, used to build a scoring scale that applied some of the rationale 

reflected in the NRC prototype, but utilized the critical effects information most likely to be 

available from databases such as IRIS, which eliminated outcome judgments that would 

confound the scoring process. In this exercise, some difficulties were encountered in scoring 

Severity, particularly with assigning the middle score categories (3, 4, 5, and 6) and with 

classifying different types of cancer. Accordingly, the scoring protocol was modified again to try 

to provide better discrimination between the effects associated with the middle scores and 

remove the medical treatment considerations. Two new scoring options were developed. One 

was a nine-point scheme and the other a five-point scheme. 
 

 

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Testing of the two new scoring schemes was conducted by EPA toxicologists in the Health and 

Ecological Criteria Division of the Office of Water. Each toxicologist was presented with all the 

critical effects given in IRIS with no knowledge of the chemical or chemicals to which they were 

attached and the revised scoring protocols. They were asked to independently score the large 

group of critical effect descriptions. The toxicologists met as a group to compare scores and 

reach consensus on the score and category that is best suited for each critical effect. The fivepoint scale was compared to the nine-point scale. After completion of this exercise, the ninepoint scale displayed in Exhibit 9 was selected based on its ease of use, more transparent 

clustering of effects within scoring categories, and consistency across the individual scores 

assigned by toxicologists. 

Exhibit 9. Final Nine-Point Scoring Protocol for Severity 

Score Critical Effect Interpretation 

1 No adverse effect ---------

2 Cosmetic effects Considers those effects that alter the 

appearance of the body without affecting 

structure or functions 

3 Reversible effects; differences in 

organ weights, body weights or 

changes in biochemical parameters 

with minimal clinical significance 

Transient, adaptive effects 

4 Cellular/physiological changes that 

could lead to disorders (risk factors 

or precursor effects) 

Considers cellular/physiological changes in 

the body that are used as indicators of possible 

adverse systemic damage 

5 Significant functional changes that 

are reversible or permanent 

changes of minimal toxicological 

significance. 

Considers those disorders in which the 

removal of chemical exposure will restore 

health back to prior condition 

6 Significant, irreversible, non-lethal 

conditions or disorders 

Considers those disorders that persist for over 

a long period of time but do not lead to death 

7 Developmental or reproductive 

effects leading to major 

dysfunction 

Considers those chemicals that cause 

developmental effects or that impact the 

ability of a population to reproduce 

8 Tumors or disorders likely leading 

to death 

Considers chemical exposures that result in a 

fatal disorder and all types of tumors 

9 Death 
 

 

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The consensus judgment of the EPA toxicologists was used to construct a compendium of nearly 

250 critical effect descriptions grouped by their severity scores (e.g., “Chronic irritation without 

histopathology changes” equals a score of 3). The final Severity protocol and compendium of 

critical effects are provided in Appendix A. 

The ordering of the nine-point scale, which clusters developmental and reproductive effects at a 

score of 7, and assigns tumors or disorders likely leading to death a score of 8 became a point of 

discussion. Some reviewers of the protocol felt that a separation of developmental and 

reproductive effects by the seriousness of the outcome was better than the clustered approach. 

This option was discussed during internal review of model outcomes (Chapter 4) by an internal 

EPA reviewer panel. The Agency reviewers decided that the benefits of the proposed scale 

outweighed potential drawbacks. The ability to clearly identify PCCL chemicals with even a 

slight developmental reproductive or tumorigenic effect through their Severity score is a benefit 

of the Exhibit 9 scoring system. 

The scoring scale’s “uneven steps” were also noted as a point of concern. A detailed exploration 

of alternative options, which included the collapse or reordering of the categories, resulted in a 

consensus judgment to retain the current scale. The current Severity scale works well in 

providing a meaningful categorization of the array of critical effects. Given the range of critical 

effects that result from a given exposure, it is not possible to have a consistent difference in the 

Severity of the outcome between each step on the scale. 

2.1.2.2 Evaluation of the Severity Scoring Protocol 

The Severity scoring protocol was evaluated using the group of chemicals that were included in 

the training data set discussed in Chapter 3 of this report. Evaluation criteria included: 

•	 Ease of scoring using the protocol and critical effect compendium 

•	 Correlation of the list or not list decisions made by workgroup members using the written 

narrative descriptions of the critical effects with those made with the numeric scores. 

•	 Outcomes from the algorithm list/no-list decisions (discussed in Chapter 4) using the 

scored data as compared with workgroup’s decisions based on the descriptive data. 

During the initial evaluation process several issues were identified. The most challenging issue 

related to Severity scores derived from LD50 Potency data. According to the scoring protocol, the 

Severity score for an LD50 Potency value would be based on the outcome of death in the test 

population and result in a Severity score of 9. The same score of 9 would be given to a LOAEL 

or RfD from a more chronic study where the critical effect was described as decreased survival 

or longevity. When the evaluator’s decisions based on descriptive information for both the 

Potency and Severity were compared to the decisions based on scores, it was apparent that the 

evaluators looked at the two effects differently. A decrease in survival from a standard chronic 

study was regarded as a more serious concern than death in a LD50 study where death is the 

targeted outcome. Several options were considered for solving this problem. The simplest option 

was to have no Severity score for an LD50 based Potency value. Another option was to retrieve 

the study that was the basis of the LD50 value and use the critical effect and dose for systemic 

effects observed rather than death. The last option was to look for a Potency value and critical 

effect from a toxicity study other than an LD50 study. 
 

 

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Experimentation with the three options for Severity based on LD50 values demonstrated that a 

combination of the second and third options provided a feasible alternative to scoring Severity on 

the basis of death when the Potency value was an LD50. The option of eliminating the Severity 

score for an LD50 value was determined to be a poor choice since it fails to make full use of the 

available data. It was decided that only when attempts failed to identify an alternate study and/or 

pre-mortality effects in the LD50 study, that an LD50 based score of 9 would be assigned. 

A problem was encountered with critical effect information for LOAELs from the RTECS 

database. This database summarized all effects without specifying which one was the critical 

effect. In cases where the original data source was available in the supplemental data, it was 

consulted to identify which effect was critical. When the supplemental data identified a NOAEL 

for the critical study it replaced the RTECS LOAEL. If the original source could not be accessed, 

an alternative NOAEL or LOAEL and its critical effect(s) were identified from the supplemental 

data and replaced the RTECS LOAEL. Two guidelines were applied when choosing the 

replacement option. In most cases a replacement was made only if the new LOAEL was lower 

than the RTECS value. However, in some cases the alternate value, although greater than the 

RTECS LOAEL was chosen because it was from a study that was higher in quality, more 

accessible and more recent than the RTECS citation. In any case where the RTECS remained the 

only source for the data, the score for Severity was based on the most serious of the cluster of 

effects presented. 

Some problems with scoring were encountered in cases where critical effects were not included 

in the critical effect compendium. The compendium of critical effects descriptors was developed 

to allow people who were not toxicologists to score chemicals based on Severity. In cases where 

the scorers could not determine a Severity score, the data were submitted to EPA toxicologists. A 

minimum of three toxicologists scored the critical effect. The consensus score was determined 

and the critical effect descriptor and its score were added to the critical effect compendium. 

One Severity scoring factor that may have had an effect on the correlation between the 

classification algorithm-based list/no-list decisions (See Chapter 4) and EPA decisions for the 

Training Data Set was the numeric Severity score of 8 for carcinogens. The only critical effect to 

score 8 was carcinogenicity. Workgroup members could easily identify carcinogens by their 

Severity score and possibly placed more emphasis on this result than the other numeric scores. 

The classification algorithm was less able to do so, particularly for carcinogens with low Potency 

values. For example, in some cases, the algorithm made a “no-list” decision when the Severity 

Score was 8 and the expert evaluators made a “list” decision primarily because of the Severity 

score’s linkage to cancer. This was particularly true in a couple of cases where all the other 

scored values were identical or close to identical but Severity was a 7 compared to an 8 (cancer). 

The decisions for the algorithm and EPA matched more closely when Severity was a 7 than 

when it was an 8 with EPA more likely to choose a list decision for the 8 Severity score than the 

algorithm. 

In most cases, the combination of Potency and Severity scores performed well in EPA exercises 

used in developing the PCCL to CCL process and the algorithm trials that followed (Chapter 4). 

Alternative approaches were adopted for dealing with LD50 based Potency values, and critical 

effect terms that were not initially in the critical effects compendium were added. Finding an 

alternative to an LD50 Severity score of 9 and consulting supplemental sources for critical effect 

information increased the effort required to obtain the Severity data, but appeared to function 
 

 

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well. These changes are reflected in the Severity Scoring Protocol and Compendium of Critical 

Effects in Appendix A. 

2.2 Occurrence Attributes 

The attributes selected to define actual or potential occurrence of contaminants in drinking water 

are Prevalence and Magnitude. Magnitude is related to the quantity (e.g., concentration) of a 

contaminant that may be in the environment. Prevalence provides a measure of how widespread 

the occurrence of the contaminant is in the environment. When direct occurrence data are not 

available, Persistence and Mobility data are used as surrogate indicators of potential occurrence 

of a contaminant. Persistence-Mobility is defined by chemical properties that measure or 

estimate environmental fate characteristics of a contaminant and affect their likelihood to occur 

in the water environment. 

 

Similar to the health effects attributes, the occurrence attributes are interrelated. The data sources 

and the learning sets used to define and scale Magnitude, Prevalence, and Persistence-Mobility, 

as well as more details about the individual attributes are described in the following sections. 

Unlike the health effects attributes, the data elements used to characterize occurrence are not 

solely based on a disciplined progressive study of the contaminants. The availability of data from 

surveys of contaminants in ambient and drinking water, the detection limits of analytical 

methods, limitations in reporting requirements, as well as indirect measures of potential 

occurrence needed to be considered and evaluated. Data sources that could provide occurrence 

data ranged from direct measures of concentrations in water to annual measures of 

environmental release or production. 

The most relevant data for characterizing demonstrated occurrence are monitoring studies or 

surveys designed to assess national occurrence in drinking water. Finished drinking water 

occurrence data sources that have been compiled include the Unregulated Contaminant 

Monitoring Regulations (UCMR), the National Drinking Water Contaminant Occurrence 

Database (NCOD) (Round 1 and Round 2 unregulated contaminant data), and the National 

Inorganic and Radionuclide Survey (NIRS). 

Finished water occurrence data are often not available for many chemicals; therefore, other types 

of data that provide the measures of potential occurrence in Public water systems (PWSs) need to 

be considered. EPA identified national monitoring studies of occurrence in ambient waters, 

which may be the eventual source waters for drinking water supplies. Two US Geological 

Survey (USGS) data sources provide information on source water occurrence for CCL: the 

National Water Quality Assessment Program (NAWQA) and studies related to the National 

Reconnaissance of Emerging Contaminants. These sources provide direct measures of 

occurrence in potential source water and indicate possible occurrence in PWSs. 

Many of the chemicals evaluated through the CCL process will not have direct water 

measurements (finished or ambient). Other available sources that provide data about the potential 

for drinking water occurrence include: 

•	 the EPA Toxics Release Inventory (TRI), that reports annual volumes of chemicals 

released from industrial applications and the number of states in which those releases 

occur; 
 

 

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•	 the National Center for Food and Agricultural Policy’s National Pesticide Use Database 

that provides estimates of the amount of pesticide applied and the number of states in 

which it is applied; and 

•	 EPA’s Chemical Update System/Inventory Update Rule (CUS/IUR), a source for annual 

production volume data under the Toxic Substances Control Act. Note the CUS/IUR data 

are categorical (i.e., chemicals are in categories with a range of production values, such 

as 500,000 to 1,000,000 pounds). 

2.2.1 Prevalence and Magnitude Data Elements 

A learning data set of 207 chemicals was compiled and used to develop and calibrate scales for 

scoring the Magnitude and Prevalence attributes. Due to the linkage of the data used, the scaling 

and scoring evaluations were performed concurrently. The linkage between Magnitude and 

Prevalence measures is shown in Exhibit 10. The Magnitude measure indicates the median 

concentration of detections in water or the total pounds of the chemical released into the 

environment. The median was selected over mean because it typically is a more stable estimate 

of central tendency in environmental occurrence data. Outliers have strong influence on means, 

often to the extent that the mean is greater than all but the maximum value (particularly when 

only detections are used in the calculation). The median of detections was selected over the 

median of all measurements in water because all measurements would include non-detections. 

Non-detections either signify that the chemical is not occurring or the analytical method is 

unable to measure the chemical below the detection limit. The inclusion of non-detections 

reduces the median value and, for the majority of environmental chemicals, the median would be 

a less than value (i.e., < the reporting or a “non-detect” value). This would provide little 

information and limited discrimination among the chemicals. Prevalence uses the same data 

source as Magnitude. The linked Prevalence measure provides an indicator of how widely the 

contaminant may be present; in general Prevalence shows the proportion of monitoring sites or 

states with detections or releases. 

Exhibit 10. Relationship of Data Elements Used to Score Magnitude and Prevalence. 

Magnitude Data Prevalence Data 

Median concentration of detections from 

finished water systems. 

Percent of finished water systems nationally 

with detections of a contaminant. 

Median concentration of detections from 

ambient water sites. 

Percent of ambient water sites nationally 

with detections of a contaminant. 

Amount of total releases nationally in TRI; 

annual, in pounds. 

Number of states reporting releases of the 

chemical in the Toxics Release Inventory. 

Sections 2.2.2 and 2.2.3 discuss the approach used to develop and calibrate the scales for scoring 

Prevalence, and Section 2.2.4 through 2.2.7 discusses the approach for Magnitude including the 

use of Persistence and Mobility Scores as a surrogate for Magnitude when Production volume is 

used for Prevalence. 
 

 

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2.2.2 Prevalence - Calibrating Scales and Scoring 

Prevalence is a measure of a contaminant’s occurrence across the United States. It uses measures 

such as: 

• Contaminant detections from Drinking Water Monitoring Programs 

• Contaminant detections from Ambient Water Monitoring 

• States where pesticides are applied 

• States reporting releases of a given chemical to the environment 

• Production of commodity chemicals in pounds per year 

These Prevalence measures have finite ranges such as zero to 100 percent of PWSs or 1 to 50 

states depending on the reporting requirements of the available data source. Accordingly, 

transformations to log-based distributions are not necessary. The scaling analyses for Prevalence 

focused on establishing groupings of the chemicals across the scoring scale. 

The analyses began with equal bin distributions. Both 100 percent of sites with detections and 50 

states with releases divide equally into ten bins based on deciles. In the case of Prevalence, the 

bins provided a fairly good fit to the distribution. However, they still required some adjustment 

because the equal bins had a tendency to segregate contaminants by type. Contaminants with the 

highest percent detections scoring a 9 or 10 were naturally occurring inorganic contaminants. For 

example, in the National Inorganic and Radionuclide Survey for ground water, ions such as 

sodium, calcium, and iron were all detected in ≥ 90% of the groundwater systems sampled. 

Contaminants with the highest releases were mostly the high-use pesticides applied in nearly all 

the agricultural states or high-use commodity chemicals with reported discharges from 

manufacturing or distribution sites in a large number of states such as the Benzene, ethyl 

benzene, toluene, and xylene impurities in petroleum products. 

Creating ten equal bins from the number of states with environmental releases resulted in a scale 

where a Prevalence score of 10 meant that releases had to be reported from 45 or more States. 

EPA revised the scale for release data so that if more than half the states (25) reported releases 

the chemical would receive a Prevalence score of 10 and indicate that the contaminants potential 

for occurrence was relatively high. The percent of detections in finished and ambient water (i.e. 

percent of systems/sites) were also adjusted to ensure that the most widely detected organic 

chemicals received more representative scores when compared to the naturally occurring 

inorganic compounds (IOCs). 

Among occurrence data elements, the linkage between the Prevalence measures and Magnitude 

measures works well for the water measurements and environmental release measures. It does 

not work well in the cases when only annual Production data are available. The Production data 

provide a measure of pounds of a chemical product produced annually in the United States but 

these data do not provide a linked measure such as the number of states in which it is produced 

or used. This production rate represents the commercial importance of the chemical to some 

extent. Since high production tonnage suggests wide use of a commodity chemical, EPA decided 

that production data would be used as a measure for likely Prevalence across the country. For 

example, a chemical produced at a billion pounds per year is more likely to be used and released 

more widely than a compound produced at only 10,000 pounds per year. Experimentation to 

examine the correlation of Prevalence scores based on measures of detections in water and the 
 

 

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number of states receiving environmental releases, based on production, supported this 

hypothesis. Correlations were only fair to good but justified the use of production data as a 

measure of Prevalence when other data on the spatial spread of a contaminant across the United 

States are not available. 

Following appropriate adjustments to insure that there was adequate representation of organic 

and inorganic contaminants across the ten point scale and a reasonable distribution of the scores 

based on release data, the Prevalence scoring scales were finalized. The Prevalence scoring 

protocol is presented in Appendix A. 

2.2.3 Evaluation of the Prevalence Protocol 

The relationship between production or even environmental release data and the actual 

occurrence in drinking water is complex. Exhibit 11 shows the scores for several contaminants 

based on the finalized Prevalence scoring scales. As expected, in some cases the agreement of 

scores across these differing data elements was not good. For example, a chemical like 

glyphosate scores very high for environmental release, but its water occurrence scores are very 

low, because of the chemical and physical properties that influence its fate and transport in the 

environment, restrictions on use locations, and drinking water treatment. 

Exhibit 11. Comparison of Prevalence Scores for Learning Set Contaminants 

Chemical Potable Total TRI Pesticide Production 

water Releases Applications 

samples 

% PWS # states # states lbs/year 

detect. 

Calcium 10 

Atrazine 9 

Glyphosate 2 

Metribuzin 1 

Toluene 9 

Trichloroethylene 9

Tetrachloroethane 3 

NA 

8 

ND 

4 

10 

10 

6 

NA 

10 

10 

10 

NA 

NA 

NA 

8 

7 

NA 

NA 

9 

8 

7 

1,1,2,2 

 

The contaminants in Exhibit 11 indicate that, when the correlation between possible Prevalence 

scores is weak, the major difference (e.g. glyphosate) is between the finished water score and the 

production/release scores. This supported the decision to use a hierarchy of data elements for 

Prevalence. Where actual water measurements are available, they are the Prevalence measure of 

choice because they are the most direct measures of likely occurrence in drinking water. 

The hierarchy selected for use in scoring Prevalence is as follows: 

• Percent of PWSs with detections (national scale data) 

• Percent of ambient water sites or samples with detections (national scale data) 

• Number of states reporting application of the contaminant as a pesticide 
 

 

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•	 Number of states reporting releases (total) of the chemical 

•	 Production volume in pounds per year 

2.2.4 Magnitude - Calibrating Scales and Scoring 

To scale the Magnitude attribute, an evaluation to identify possible correlations among data 

elements was conducted. First, a comprehensive universe of finished water quality data was 

compiled, including the national occurrence database of regulated contaminants (compiled for 

the first 6-Year Regulatory Review), the historic data from various unregulated contaminant 

monitoring programs (noted as NCOD Rounds 1 and 2, above), and the data from NIRS. This 

provided a comprehensive array of data covering the expected distribution range of Magnitude 

for any new contaminant, ranging from high median concentrations for some naturally occurring 

inorganic ions or elements to non-detect values for some trace organic chemicals. 

The NRC (2001) had initially recommended that Magnitude be scored based on its relationship 

to Potency. In their pilot study they proposed that the magnitude score be the square root of the 

median concentration, (based on its position in a decile distribution) times the potency score. A 

median concentration that fell within the lowest decile of the distribution would receive a 1 and 

that in the highest decile a 10 for the calculation. EPA evaluated the NRC approach to scoring 

Magnitude and found that it was not feasible for the following reasons: 

•	 The NRC equation cannot be applied when the Magnitude data are based on
 

environmental release or chemical/physical properties.
 

•	 A decile distribution for the median concentration values results in low scores for almost 

all organic chemicals because of the high concentration of geochemical inorganic 

contaminants present in water (see Exhibit 12) 

•	 Application of the NRC equation did not provide a good measure of relative Magnitude 

(see aldrin and sodium in Exhibit 12). A high concentration, low Potency combination 

can receive the same score as a low concentration, high Potency combination. 

To examine the efficacy of the NRC approach, EPA applied it to six of the chemicals from CCL 

1 for which regulatory determinations had been made and the magnitude scores, thus, had the 

necessary Potency and occurrence data. The results of that evaluation are summarized in Exhibit 

12. 
 

 

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Exhibit 12. Comparison of the NRC Magnitude Score with the Ratio of the Health 

Advisory Guideline to the Concentration in Finished Water 

Contaminant Potency Benchmark Median 

Concentration 

Magnitude Potency 

Benchmark: 

Concentration 

Ratio1 

mg/L Score mg/L Score NRC 

score 

Aldrin 0.000002 10 0.0006 1 3.2 0.003 

Hexachlorobutadiene 0.0009 7 0.001 1 2.6 0.9 

Manganese 0.3 4 0.01 1 2 30 

Metribuzin 0.07 5 0.001 1 2.2 70 

Naphthalene 0.1 5 0.001 1 2.2 100 

Sodium 120 1 16.4 10 3.2 7.3 

1

The Potency Benchmark is the Health Advisory guideline (cancer or non-cancer) for a 

lifetime exposure for all chemicals except sodium. The guideline for sodium is derived 

from the recommended dietary intake for sodium in adults, 2.4 g/day ÷ 2L/day using a 

Relative Source Contribution of 10%. 

The Potency Scores were derived from the RfD-equivalent or 10-4 cancer risk values. 

The concentration scores were obtained by using sodium as the upper level for the range 

and dividing the range into deciles as recommended by NRC. 

As indicated in Exhibit 12, the NRC score does not display a consistent relationship to the ratio 

of the potency-based drinking water guideline to the median finished water concentration. 

Aldrin, the contaminant from Exhibit 12 that is present in drinking water at the levels of greatest 

concern has the same magnitude score as sodium ion that is only weakly toxic and not present at 

a concentration of concern for other than those on very low sodium diets. In addition, as 

mentioned above, the decile distribution of concentrations resulted in a score of 1 for any 

contaminant present in water at concentrations lower than 1.6 mg/L (one tenth of the sodium 

concentration). Given this distribution, only inorganic contaminants are likely to receive 

intermediate scores on the concentration scale. Because of the observed limitations in the NRC 

proposed approach EPA determined that it was not appropriate for scoring Magnitude. 

The second approach that was investigated employed the use of the Health Reference Level 

(HRL) to establish the scores for Magnitude. For example, the largest dose that received a 

Potency score of 10 was converted to a mg/L equivalent using the HRL methodology. Anything 

less than that concentration received a 1 on the Magnitude scale. Each log-based Potency value 

was paired with a log-based concentration. A Potency score of 10, when paired with any 

Magnitude score, would be suggestive of concern because the concentration was greater than the 

Potency. However a Potency score of 8 would only give rise to concern if the Magnitude score 

was 3 or greater (see Exhibit 13). 
 

 

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Exhibit 13. Magnitude Concentrations and Scores Derived from Potency Doses 

Potency 

Score 

Potency Range 

mg/kg/day 

Concentration equivalent 

mg/L 

Magnitude 

Score 

10 0 to 3.16 x 10-7 0 to 2.2 x 10-6 1 

9 3.17 x 10-7 to 3.16 x 10-6 >2.2 x 10-6 to 2.2 x 10-5 2 

8 3.17 x 10-6 to 3.16 x 10-5 >2.2 x 10-5 to 2.2 x 10-4 3 

This second approach to relating Potency and Magnitude proved to be unwieldy because the two 

scales are inversely related. It was also problematic because it could not be used for Potency 

values based on NOAELs, LOAELS, and LD50s, or Magnitudes that were not expressed in 

concentrations terms. It also did not take into account the differences in the HRL determination 

process for carcinogens versus non-carcinogens. 

EPA next explored a variety of potential scales that could be applied to the finished water 

concentration data without consideration of Potency. EPA converted the finished water data to a 

standard unit of measure (μg/L) and evaluated several ranges of concentrations to correspond to 

magnitude scores. Exhibits 14A through 14C illustrate the comparisons of three of the 

approaches evaluated for the organic and inorganic contaminants. Exhibit 15 shows the 

differentiation in scores across the three experimental approaches. 

The first approach was to develop scales that utilized the array of compiled Magnitude data and 

10 bins with approximately equal numbers of contaminants in each bin, referred to as the equal 

number bins scale in Exhibit 14A. Equal bins did not provide a good dispersion of scores. 

Accordingly, various log-scale options were explored. The Magnitude data do not range across 

as many orders of magnitude as the Potency RfD data, so various semi-logarithmic scales were 

evaluated to better represent the distribution of values across the scale. 

In evaluating and developing the calibration scale, the water occurrence data presented a 

particular challenge because the IOCs tended to skew the results. Many IOCs result from various 

anthropogenic processes, but most are of geologic origin as well, and they have relatively high 

measures for both Prevalence and Magnitude compared to most organic chemicals. Hence, for 

some of the semi-logarithmic Magnitude scales (e.g., Half-Log Option A), the only chemicals 

that could score high (e.g., a 10 or 9) would be IOCs. Such a scale would depress the score for 

organic chemicals that are of equally high concern because of their expectedly lower 

concentrations. One approach that EPA evaluated was using different scales for IOCs and 

organic chemicals; however, having two scales would make the scoring process overly complex. 

To keep the process straightforward and transparent it was decided to use one scale for all water 

data. Accordingly, the scores were distributed across the range of values so that organic 

contaminants could receive high scores as well as the IOCs. Comparisons and adjustments were 

made until the current protocols, using a semi-logarithmic scale (Half-Log Option B shown in 

Exhibit 14C), were selected. 
 

 

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50 

40 

Count of Contaminants 

< 0.2 

1 

0.20-<0.50 

2 

0.50-<0.51 

3 

0.51-<0.75 

4 

0.75-<1.0 

5 

1.0-<1.01 

6 

1.01-<1.25 

7 

1.25-<1.60 

8 

1.60-<2.50 

9 

> 2.50 

10 

30 

20 

10 

0 

Category and Break Points μg/L 

Organics Count Inorganics Count 

Exhibit 14A. Equal Bins Drinking Water Magnitude Scale (ug/L)

Exhibit 14B. Half Log Option A Drinking Water Magnitude Scale (ug/L)

75 

60 

45 

30 

15 

0 

< 0.10 0.10-<0.3 0.3-<1 1-<3 3-<10 10-<30 30-<100 100-<300 300-<1000 > 1,000.0 

1 2 3 4 5 6 7 8 9 10 

Category and Break Points 

Count of Contaminants 

Organics Count Inorganics Count 
 

 

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Exhibit 14C. Half Log Option B Drinking Water Magnitude Scale (ug/L)

0 

15 

30 

45 

60 

75 

< 0.003 0.003-

<0.01 

0.01-<0.03 0.03-<0.1 0.1-<0.3 0.3-<1.0 1.0-<3.0 3.0-<10.0 10.0-<30.0 > 30.0 

1 2 3 4 5 6 7 8 9 10 

Count of Contaminants 

Category and Break Points 

Organics Count Inorganics Count 

Exhibit 15. Magnitude Attribute Scores: Example Contaminants Scored by 

their Median of Detections Using the Various Approaches in Exhibit 14. 

Half-Log Half-Log “Bins” Chemical Option A Option B Score Score Score 

Hexachlorobutadiene 2 2 5 

1,1,2,2-Tetrachloroethane 3 3 6 

Boron 10 6 10 

Sulfate 10 10 10 

Antimony 9 4 7 

Ethylbenzene 6 4 6 

Endothall 10 6 9 

Methyl ethyl ketone 5 3 6 

When developing the calibration scales for the release data, the ranges of data were similarly 

arrayed using a scale based on half-log units with a distribution of scores that reflected the 

distribution of the data in the learning set. 
 

 

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2.2.5 Persistence-Mobility as a Surrogate Measure for Magnitude 

In cases where production data are the only measure of occurrence, scoring for Prevalence and 

Magnitude becomes difficult. The NRC discussed Persistence and Mobility as a fifth attribute 

and had suggested it could be used to predict possible occurrence if other direct measures were 

not available. In its review, NDWAC suggested that Persistence and Mobility could provide a 

surrogate measure of Prevalence with production used as a measure of Magnitude. To examine 

the NDWAC proposal, EPA carried out a series of exercises in which scores for Magnitude 

derived from concentrations in drinking water and environmental releases were examined to see 

if they correlated with production scores and with Persistence-Mobility scores calculated using 

the scoring equation developed by NDWAC. In no case was the correlation as good as one might 

desire, but it was apparent that the Persistence-Mobility approach showed a better correlation 

with the Magnitude scores, based on the preferred data elements (concentration/release), than the 

production information. Accordingly, EPA chose to use Persistence-Mobility as a surrogate 

measure for Magnitude. 

Persistence and Mobility are environmental fate parameters. They are considered in combination 

as a measure of potential occurrence because both transport (i.e. Mobility) and fate (i.e. 

Persistence) are important when predicting whether a contaminant is likely to be found in water 

at a specific location, in situations where there is an environmental source for the contaminant. 

The length of time a chemical remains in the environment before it is degraded (Persistence) 

affects its importance as a potential drinking water contaminant. Persistence is generally 

expressed as rate of degradation or half-life (t1/2) indicating, in this case, the length of time 

required for the chemical to degrade to half its original concentration in the medium of interest 

(e.g. water). Similarly, the Mobility of a chemical, or its ability to be transported to and in water, 

affects its potential to reach and dissolve in the source waters for a PWS. 

There are a number of data elements that measure the fate of a chemical in the environment. The 

physical/chemical parameters that are most relevant to the fate in drinking water are summarized 

in Exhibit 16. The first 4 measures of mobility represent the equilibrium ratio for the partitioning 

of the contaminant from one medium to another: Koc (sediment: water), Kow (octanol: water), Kd 

(soil: water) and Henry’s Law Coefficient (air: water). Koc, Kow and Kd are sometimes expressed 

as logs of the original measurements. The measures of persistence each reflect the time the 

chemical will remain unchanged in the environment. 

Exhibit 16. Mobility and Persistence Data Elements 

MOBILITY PERSISTENCE 

Organic Carbon Partition Coefficient (Koc) Half-Life 

Octanol/Water Partition Coefficient (Kow) Measured Degradation Rate 

Soil/Water Distribution Coefficient (Kd) Modeled Degradation Rate 

Henry's Law Coefficient (KH) 

Solubility 
 

 

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The data elements listed in the table above are arranged in hierarchical order, with the most 

desirable at the top (i.e., the first data to be used if available). 

Organic Carbon Partitioning Coefficient (Koc) is one of the most common indicators of the 

mobility of a chemical in water. A high Koc increases the probability that, once a chemical 

reaches a receiving water body, it will remain bound to sediments or adjacent soils, and thus, 

slowly partition from the sediment to the water column. A high Koc favors the presence of the 

contaminant in water for a long time but at low concentrations since the Koc will favor the 

sediment over the water. A high solubility favors rapid dissolution in the water body from a nearby source and potentially high concentrations if the water source is confined and the 

environmental release substantial. 

2.2.6 Persistence-Mobility Data – Calibrating Scales and Scoring 

Many of the measurements of environmental fate properties vary depending on the actual field or 

laboratory conditions. Some are reported in standard data sources only as ranges, or categorical 

descriptions. Scoring was further complicated by the fact that two separate environmental fate 

parameters were used in the scoring of the one attribute. Accordingly, EPA selected the approach 

proposed by NRC and supported by the NDWAC for using the Persistence-Mobility information 

after experimenting with several other approaches. 

The Persistence and Mobility data were arrayed, or partitioned into relatively simple lowmedium-high categories as suggested by NRC. Published definitions for the categories were 

used, such as the categories for Koc from Fetter, 1994 and the classifications for the octanol water 

partition coefficient (Kow) from Lyman, et al, 1990. The categories are given values of 1, 2, or 3 

based on the ranking of the measurement from low to high. The persistence value is averaged 

with the mobility value and a multiplier (10/3) is used to translate the score to a 10 point scale 

(see the Persistence-Mobility Protocol in Appendix A, for details). 

Since the persistence and mobility data are being used as a measure of Magnitude, a low ranking 

(1) for a parameter is one that will minimize the concentration in water and a high ranking (3) is 

one that will maximize the concentration. For example, a high Koc means that the distribution 

between the water column and sediment favors the sediment and is ranked a low, while a lower 

Koc means that the ratio of a contaminant in sediment to that in the water allows a larger portion 

of the total to be in the water and is ranked as high. 

As mentioned above, EPA undertook a series of evaluations to compare the Persistence-Mobility 

scores for selected contaminants to the Magnitude scores derived from the preferred data 

elements (concentrations in water or environmental releases). Often, data were not available for a 

half-life or a measured degradation rate for the Persistence value. In these cases, EPA’s PBT 

Profiler was tested and added to the Persistence protocol to ensure both Mobility and Persistence 

data were used to calculate the attribute score (www.pbtprofile.net). 
 

 

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The PBT Profiler was developed as a screening tool to identify pollution prevention 

opportunities for chemicals without experimental data. Among other endpoints, it estimates 

environmental Persistence for organic chemicals.7 In addition to estimating a degradation rate, 

the PBT Profiler also estimates the percentage of a chemical that partitions to soil, sediment, 

water, and air compartments. As a last option, in cases where other chemical property data are 

not available, the amount of a chemical that is predicted to partition to the water phase by the 

PBT Profiler (the percent in water, a measure of solubility) is used to score Mobility. 

EPA recognized that the Persistence-Mobility protocol can result in relatively high scores (7 to 

10) in cases where more direct data elements for scoring are not available. However, given the 

uncertainty associated with some of the Persistence-Mobility data elements, EPA decided the 

somewhat conservative scores were acceptable as surrogate measures for Magnitude, when only 

these data were available for scoring. 

2.2.7 Evaluation of the Magnitude Protocol 

The occurrence data clearly vary in how directly they measure demonstrated or potential 

occurrence related to drinking water. Exhibit 17 compares the scores for several chemicals using 

the different measures of Magnitude. In all cases the finished water Magnitude score is higher 

than the score for ambient water. Scores for pesticide application rates are higher than those for 

TRI releases. As was the case for Prevalence, EPA determined that a hierarchy would be used in 

scoring Magnitude. The hierarchy developed uses finished water occurrence data if available. 

Exhibit 17. Comparison of Scores derived using the Magnitude Protocol 

Finished Ambient Pesticide Total Persistence/ Chemical CASRN Water Water Release TRI Mobility Concentration Concentration Data 

Median (μg/L) Median (μg/L) Lbs/year Lbs/year 

Calcium 7440702 10 -- -- -- 10 

Atrazine 1912249 6 4 10 8 8 

Glyphosate 1071836 2 -- 10 -- 7 

Metribuzin 21087649 7 3 8 2 7 

Toluene 108883 6 4 -- 7 5 

Trichloroethylene 79016 7 4 -- 10 10 

1,1,2,2 

Tetrachloroethane 79345 6 5 -- 4 7 

7 The PBT program will not accept inorganics as input, and identifies the elements, which if present, that prevent the 

profiling of a particular chemical. The only exceptions to this rule are sodium, potassium, and ammonium salts of 

organic acids, which can be profiled. Thus, the PBT profiler cannot be used for inorganics or organometallics. 

However, as drinking water ions, inorganic contaminants are generally present as salts and do not degrade, and thus 

are assigned a score of “3” – high persistence. See the Appendix A for more complete review. 
 

 

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The hierarchy suggested for Magnitude draws on the following data sets: 

• Median concentration of detections from finished water systems 

• Median concentration of detections from ambient water sites or samples 

• Amount of pesticide applied 

• Amount of total releases 

• Persistence-Mobility data 

2.3 Fine Tuning the Protocols 

As discussed in the previous sections, EPA developed and fine-tuned the Attribute Scoring 

Protocols through a step-wise process of data selection, data analysis, calibration of scales, and 

evaluation of the functionality of the scores in PCCL to CCL decision-making. The decisionmaking component of the process examined the ability of the scored attributes to adequately 

represent the level of concern about contaminants. The testing also evaluated whether or not the 

scores provide a consistent input to the decision making portion of the CCL listing process that is 

relatively independent of the type of input data that provides the basis for the score. 

Quality assurance measures utilized comparisons of list - not list determinations by a panel of 

EPA subject matter experts based on descriptive and quantitative measures of health effects and 

occurrence (raw data) compared with determinations based on the scored attributes. Differences 

in decisions were identified. The panel discussed those differences and the rationale they had 

used to reach decisions based on the raw data versus the scored data. Minor adjustments were 

made to the scoring protocols based on those discussions. 

Using a training data set of contaminants (Chapter 3), blinded test-case decisions made with raw 

data versus scored results, or decisions based on one data element in a hierarchy versus another, 

were compared. The results provided a high level of confidence that the scores, while not 

capturing all information experts used in making decisions based on raw data, adequately 

captured the critical relationships that informed “list” versus “don’t list” determinations made by 

the EPA panel. 

3.0 DEFINITIONS AND OVERVIEW OF THE TRAINING DATA SET 

This chapter describes the process used to identify a set of chemicals to train (or calibrate) the 

classification models discussed in the next chapter. The raw data, attribute scores, and protocols 

discussed in chapter 2 were applied to these contaminants and that information is carried forward 

in the evaluation of classification models discussed in Chapter 4. 

The training data set (TDS) for chemicals is the set of data used to train (or teach) the 

classification models to mimic expert list-not list decisions. The TDS used to train the models for 

CCL 3 was comprised of 202 discrete sets of attribute scores for contaminants and consensus 

list-not list decisions made by a team of EPA subject matter experts. 

Classification models are algorithms that use statistical approaches for pattern recognition and 

derive mathematical relationships among input variables (measurements or descriptive data) and 

output from a TDS. For the CCL, the classification models are used to develop a relationship 
 

 

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between the contaminant attribute scores (input variables) and the classification of these 

contaminants into list-not list categories (output). The mathematical relationship between 

attribute scores and list-not list decisions is determined based on the classification decisions on 

TDS chemicals and their associated data. Once the TDS is used to train the classification model, 

the model is then applied to a larger list of contaminants to predict their classifications, list or not 

list. 

The process for developing the TDS utilized EPA subject matter experts familiar with the 

technical aspects of the attribute data and the selection of drinking water contaminants for listing 

and regulation. The subject matter experts represented drinking water, toxicology, public health, 

engineering, and statistics disciplines. 

3.1 Key Considerations 

EPA considered the following key factors in developing the training data set: 

•	 Selection of contaminants representing a range of outcomes and decisions likely to be 

encountered in developing a CCL; 

•	 A variety of input data ensuring adequate coverage of possible attribute scores and 

combinations of scores; 

•	 Chemicals that, when present in drinking water, would present a meaningful 

opportunity for public health improvement if regulated; and 

•	 Contaminants that would likely be selected for the PCCL. 

3.2 Developing Key Components of the Training Data Set 

3.2.1 Attribute Scores 

Attribute scores are a critical component of the TDS, as mentioned in Chapter 2. The TDS used 

for training the classification models consisted of attribute scores for 202 contaminants. A set of 

known chemicals was chosen to develop the TDS and supplemented with a range of attribute 

scores that represented hypothetical or artificial contaminants. These artificial contaminants were 

developed to fill voids in the space of possible attribute scores and improve classification model 

results. 

3.2.2 Attribute scores for real contaminants 

Initially, EPA selected “data rich” contaminants from among regulated contaminants and 

previous CCLs because they had a range of readily available occurrence and health effects 

information. EPA drinking water subject matter experts and stakeholders (as part of the NDWAC 

process) reviewed the initial list of contaminants and identified candidates for the TDS. Based 

upon an NRC and NDWAC recommendation, EPA also added chemicals “generally regarded as 

safe” by the U.S. Food and Drug Administration to provide adequate coverage of possible 

attribute inputs and a range of list-not list decisions. This initial selection process identified 51 

chemical contaminants for the TDS. 

Subsequently, EPA chose 50 additional contaminants from the CCL 3 Universe. These 50 

contaminants were randomly selected from those with high health effects toxicity levels that had 
 

 

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occurrence data because they represented contaminants likely to make it to the PCCL. The 

addition of these 50 contaminants resulted in 101 contaminants with data to score attributes. 

To aid in the review and evaluation, data summary sheets were prepared for each contaminant 

that included available health effects, occurrence, and environmental fate data. All the available 

health effects and occurrence, use, and fate data that could be used to develop the attribute scores 

for Potency, Severity, Magnitude and Prevalence were included on the individual summary 

sheets. The data summary sheets are presented in Appendix B. 

While contaminant names were included in the initial evaluations, EPA subject matter experts 

found that knowledge of the contaminant name introduced bias into the decision-making process. 

Subsequently, EPA “blinded” contaminant names or identifiers in contaminant evaluations to 

increase objectivity and force decisions to be made solely on the available data and associated 

attribute scores. The names of contaminants were revealed after the “blinded” evaluations. The 

attribute scores were developed according to the Attribute Scoring Protocols discussed in 

Chapter 2 and presented in Appendix A. 

3.2.3 Attribute scores for hypothetical contaminants 

The performance of the classification models using the initial TDS gave an indication of gaps in 

the possible attribute space that the set of 101 TDS contaminants did not adequately cover. This 

led EPA to add a set of 101 hypothetical contaminants to the TDS. These contaminants had 

specific combinations of attribute scores designed to fill gaps in the space defined by all possible 

attribute scores and to improve the performance of the models. EPA identified 16 general ranges 

of scores using all four attributes and permutations of high or low scores. The majority of these 

possible scores were selected using Latin hypercube sampling from the set of all possible 

attribute score combinations, as seen in Exhibit 18 (NIST, 2006). Five contaminants were 

selected at random from each of the 16 “cubes” represented by the combinations of high (6-10) 

and low (1-5) scores for the four attributes. This selection resulted in 80 hypothetical 

contaminants. Twenty one additional contaminants were deliberately selected to fill in some 

obvious voids in the 4-attribute space, resulting in 101 artificial contaminants. 
 

 

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Exhibit 18. Combinations of low and high attribute scores1 for the four 

attributes using Latin Hypercube Sampling. 

Potency Severity Prevalence Magnitude 

Low Low Low Low 

Low Low Low High 

Low Low High Low 

Low Low High High 

Low High Low Low 

Low High Low High 

Low High High Low 

Low High High High 

High Low Low Low 

High Low Low High 

High Low High Low 

High Low High High 

High High Low Low 

High High Low High 

High High High Low 

High High High High 

1 Low scores are randomly sampled from the range 1-5. High scores are randomly sampled 

from the range 6-10. 

Exhibit 19 displays the attribute space coverage of the 101 contaminants compared to the 

attribute space coverage of the TDS of 202 contaminants. The combination of real and artificial 

contaminants resulted in 202 scored candidates that became the TDS. The total attribute space 

for a model that includes four attributes with scores from 1 to 10 is 10,000 combinations of 

possible attribute scores. Each point plotted in Exhibit 19 represents one chemical in the TDS 

and one of the 10,000 possible combinations of attribute scores. 
 

 

 

 

Exhibit 19. Attribute Space for the 101 TDS compared to that for the 202 TDS

Color of square indicates its classification decision. List = red; List? = beige; Not List? = light 

blue; and Not List = dark blue (also see Exhibit 29). 

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This graphical analysis shows five elements of the model results, the four attributes evaluated 

and the categorical decision (L, L?, NL?, and NL) in a single graph. Note in Exhibit 19 that the 

vertical and horizontal axes show two attributes on each axis. The attribute scores for Potency 

are the large squares across the horizontal axis. The corresponding score for Severity is a 

separate scale within each larger square. That is, each Potency square has a range of Severity 

scores. Similarly the Prevalence and Magnitude scores are plotted on the vertical axis, 

Prevalence as the large squares along the vertical axis and Magnitude as a separate square within 

each larger square. The decision category assigned each potential attribute is color coded (NL 

decisions are denoted by dark blue, NL? by lighter blue, L? by beige, and L decisions by red). 

3.3 Making List-Not list Decisions 

List-not list decisions are the second key component of the TDS, as mentioned in Chapter 3. The 

EPA subject matter experts made list-not list decisions on an individual basis and as a group, 

based on attribute scores and based on data that had not been converted to attribute scores (actual 

or raw data). The development of the list-not list decisions was an iterative process that 

incorporated revisions to the attribute scoring protocols, and the final list-not list decisions, as 

experience was gained by the EPA experts. Differences between the decisions based on the 

scored attributes and the raw data were resolved by revising the scoring protocols to improve the 

correlation of scores to the raw data. 

After evaluating the health effects and occurrence data for each contaminant, each individual 

subject matter expert made decisions about how to classify the contaminant, and then met as a 

group to discuss their decisions. Early in the process the subject matter experts recognized that 

clear list or not list classification decisions could easily be made for some contaminants, but not 

for other contaminants. The chemicals in the later group were placed into categories of List? (L?) 

or Not list? (NL?), in which L? signifies that the decision is leaning towards listing but with 

some uncertainty, and NL? signifies that the decision is leaning towards not listing, but with 

some uncertainty. These additional two categories were incorporated into the evaluation process. 

As part of the iterative process, the subject matter experts discussed their classification results 

and made adjustments to the process, accordingly. When adjustments changed attribute scoring 

protocols, TDS contaminants were rescored and reevaluated. Individual decisions were made 

separately based upon either the raw data or attribute scores. Decisions based upon raw data 

utilized health effects and occurrence data elements, as well as supporting information on fate 

and uses. For decisions based on attribute scores, only the numeric individual scores were used. 

The scores were developed from the raw data using the protocols, for Potency, Severity, 

Prevalence, and Magnitude. In both cases, this evaluation was conducted “blinded,” meaning 

contaminant names were not shown. Appendix C summarizes decisions based upon raw data and 

attribute scores. For each contaminant, comparisons were made between the list – not list 

decisions based upon raw data and those based on scores. Subject matter experts discussed the 

similarities and differences on an individual contaminant basis, and revised the attribute 

protocols to reflect decisions made on the actual data (see Chapter 2). 

Once list or not list classification decisions were made based on the attribute scores using the 

revised protocols, consensus among the EPA subject matter experts was used as the final 

decision for each contaminant. This consensus decision was used to train the models and is 

further discussed in Chapter 4. Consensus decisions were made by averaging the numerical 

decisions of individual reviewers (L = 4, L? = 3, NL? = 2, and NL=1) and rounding to the 
 

 

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nearest integer. The rounded averages became the consensus values used to train and evaluate 

the models (Chapter 4). Appendix C also provides the consensus decisions for each TDS 

contaminant. 

4.0 PROTOTYPE CLASSIFICATION MODELS AND THE CCL PROCESS 

The NRC recommended EPA use prototype classification models for CCL selection, citing the 

limitations of expert processes and other rule-based models. NDWAC agreed that EPA should 

use a prototype model, also noting that this should improve the reproducibility and transparency 

in the process. This kind of approach does not eliminate subjectivity but rather, makes the 

judgments more explicit. 

Prototype classification models are often described as pattern recognition models. These models 

develop statistical relationships (to recognize the patterns) among input variables (attributes, 

discussed in Chapter 2) of drinking water contaminants to predict their classification (“List,” 

“List?,” “Not List?,” and “Not List”). The model determines the relationship or rule that links the 

input to the output based on the decisions made on the TDS (Chapter 3) and then uses that 

relationship to classify PCCL contaminants based on their attribute scores. 

In its study, the NRC experimented with a linear discriminant model and an artificial neural 

network (ANN) model to demonstrate the use of classification approaches. EPA, working with 

NDWAC, identified the following classes of models for evaluation (NDWAC 2004): 

• Artificial Neural Networks, 

• Classification Decision Trees (with univariate and multivariate splitting rules), 

• Linear Models, and 

• Multivariate Adaptive Regression Splines (MARS). 

The model evaluation was a two-step process. First was the evaluation and selection of the most 

appropriate (“best-fit”) model from within each of the model classes. The second step was the 

evaluation of the performance of the best models selected from each class. Following these 

evaluations, two of the models were rejected and three were maintained to inform the final expert 

review process. 

Artificial Neural Networks (ANNs) - ANNs are information processing models conceptually 

based on the human nervous system and its learning processes. ANNs apply flexible and often 

very complex parameterization. Their value is that they use flexible, non-linear functions that 

can capture almost any kind of underlying relationship between input and output data. For 

classification purposes, ANNs apply weighting in non-linear functions and do not specify a strict 

functional form (such as quadratic or cubic equations) as do many statistical models. 

Classification Decision Trees - The decision tree classifies the sample by devising a series of 

tests (or rules, from the TDS) that are mutually exclusive in outcome. The graphical tree is 

derived with a test at a node in the tree with outcomes from the test branching from each node. 

Hence, in moving through the tree a contaminant encounters the test at a node, and is sent down 

one branch or another based on how its attribute(s) meets the test criterion, usually a simple 

inequality, such as is Magnitude < 3.5 (true or false). Eventually the contaminant reaches a 
 

 

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terminal node (the last node, that no longer branches) that assigns the classification (e.g., 

category 2 = NL?). Two types of decision tree models were explored, Classification and 

Regression Tree (CART) which utilized univariate (one attribute at a time) tests at nodes, and the 

Quick, Unbiased, Efficient Statistical Tree (QUEST) model, which utilized multivariate 

(weighted sum of all attributes) tests at all nodes of the tree. 

Linear Models – General Linear Models - Two types of linear models were tried. A Logistic 

regression model was applied to deal with CCL’s categorical data. The Logistic model was only 

attempted using two categories (List and Not List). EPA found that the binary approach was not 

satisfactory, and moved to a four category approach. Recognizing that the ANN models often 

employ logistic regression, to avoid duplication, the Logistic model was dropped from the final 

evaluations. Consequently, the data were adapted for use with a regular Linear Regression 

model. This model estimates EPA’s average classification (on a scale of 1 to 4; 1 = Not List, 2 = 

Not List?, etc.) for each contaminant as a linear combination of the contaminant’s four attribute 

scores. 

Multivariate Adaptive Regression Splines (MARS) - MARS is a non-parametric classification 

model sometimes referred to as a statistical neural network model. MARS has become widely 

used in data mining and exploratory analysis because it doesn’t assume or impose any particular 

class of relationship (such as linear or logistic) on all the predictor variables and the outcomes. It 

can develop different regression relationships for different input variables. 

4.1 Model Training and Development 

Some software packages are designed to build, fit, and test models internally, while others 

require an expert user to develop the model. Generally, models are evaluated based on: 

• the number of attributes that the model is able to consider, 

• the types of relationships or mathematical functions that the model utilizes, and 

• the model’s ability to predict classification of the TDS. 

 

For example, training a model can involve estimating the values of rule coefficients (such as β0 

and β1 in the simple linear regression model Y = β0 + β1X + ε), or determining some other aspect 

of model structure (such as the number of splits in a regression tree model) to improve how well 

the model classifies the existing data. Ideally, this training process minimizes the model’s 

predictive error, thereby reducing incorrect model predictions. 

“Over-fitting” is a concern when selecting a model. Any of the model classes can be made to fit 

a particular data set very well by making the model more complex (this usually means estimating 

more model parameters). However, the addition of model complexity can come at the cost of a 

loss of general applicability; the added complexity may capture the idiosyncrasies of the specific 

data set, but may not be representative of the broader processes that generate the data, and hence, 

may not perform well when applied to an unknown sample. Several methods were used as 

guidance to avoid over-fitting, depending on the specific type of model being tested. 

Software designed specifically for CART, ANN, and MARS were used for those methods. 

Appendix D lists the specific software sources that were used. These programs provide the user 

with a number of options to control the model building process. For example, QUEST software, 

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used to produce a classification decision tree model with linear discriminant nodes, allows the 

user to specify the following: 

• Minimal node size of the tree 

• Splitting method (linear or univariate discriminants) 

• Splitting criterion (likelihood ration, Pearson chi-square, etc.) 

• Pruning method (by coefficient of variation or by test sample) 

• Number of fold for cross-validation 

After the user selects the control options, the software does its best to fit the training data set. In 

general, the user is not able to view precisely how the software does its job, but is shown the 

final model, some statistics regarding its performance, and an indication of other alternatives that 

were considered. For example, the QUEST software outputs a list of decision trees and their 

summary statistics (numbers of nodes, error rates). QUEST also identifies the optimal tree and 

provides the tree’s decision rule. In addition, QUEST reports the results of cross-validation tests, 

in which subsets of the training data are held back. The algorithm produces a rule to best fit the 

remaining data and this rule is then applied to the data that were held back. This gives a slightly 

greater error rate because (a) fewer data are used to estimate the model parameters and (b) data 

used for checking are independent of those used to estimate the parameters. Exhibits 20a and 20b 

compare QUEST Classifications based on the full training data set (Exhibit 20a) and 5-fold 

cross-validation (Exhibit 20b). 

Exhibit 20a. QUEST Classifications Based on the Full Training Data Set 

(shaded cells are exact match with Expert Decisions) 

Consensus 

Blinded Model Decisions 

Decisions 

4 (L) 3 (L?) 2 (NL?) 1 (NL) 

4 (L) 42 0 0 0 

3 (L?) 13 41 2 0 

2 (NL?) 0 8 54 3 

1 (NL) 0 0 2 37 

Exhibit 20b. QUEST Classifications Based on 5-Fold Cross-Validation 

(shaded cells are exact match with Expert Decisions) 

Consensus 

Blinded Model Decisions 

Decisions 

4 (L) 3 (L?) 2 (NL?) 1 (NL) 

4 (L) 41 1 0 0 

3 (L?) 14 37 5 0 

2 (NL?) 0 10 50 5 

1 (NL) 0 0 8 31 
 

 

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Unlike other models, the simple linear model did not depend on special software. Under this 

model, the average classification of the subject matter experts for a contaminant was estimated as 

a linear combination of attribute scores. Letting Y[i] be the subject matter expert’s average 

classification for training set contaminant i, the model equation is: 

Y[i] = b0 + bPot * Pot[i] + bSev * Sev[i] + bPrev * Prev[i] + bMag * Mag[i] + ε

An intercept term (b0) and coefficients for the four attributes (bPot, bSev, bPrev, and bMag) were 

selected to maximize the likelihood of the TDS average classifications, given normal error 

structure (ε is an error term that is normally distributed with mean zero). A residuals plot 

revealed that unanimous List and unanimous Not List contaminants were often predicted to have 

extreme errors, suggesting that perhaps the subject matter experts would have assigned some of 

these to more extreme categories, had they been available. Without censoring, the unanimous 

Lists were treated as observations of exactly 4.0 and the unanimous Not Lists were treated as 

observations of exactly 1.0. Recognizing that these may be censored values, they are treated as > 

4.0 and < 1.0, and the likelihood function is adjusted to include these as probability masses 

(probability of at least 4.0 and probability of at most 1.0) rather than probability densities 

(probability of exactly 4.0 and exactly 1.0). Maximum likelihood parameters appear to fit the 

data very well, and predict most TDS average decisions to within 0.25 units. 

4.2 Model Sensitivity Analyses 

Some analyses that were performed in the development process may be considered sensitivity 

analyses. These included the following: 

•	 Training the models on subsets of the TDS. This included the partial TDS (as it was 

being developed) and cross-validation exercises, wherein randomly-selected 

contaminants were held back from training to provide independent error checks. 

•	 Training after selected “outliers” are removed from the TDS. Those selected outliers 

found to have strong influence on the overall performance were investigated further to 

see if there were valid reasons for excluding them from the TDS. 

•	 Graphical and statistical analyses. These analyses were used to identify significant 

differences in attribute “weights” or influence on model performance. If any attribute had 

been found to be insignificant, it could have been ignored, perhaps saving some data 

development resources. (Though attributes were found to have different weights, none 

was found to be insignificant.) 

Rather than detail all of the sensitivity analyses conducted for all classes of models, the 

remainder of this chapter illustrates the analyses described above using selected applications. 

4.2.1 Training with subsets of the TDS 

Cross validation for QUEST is described under 4.1, above. Training with early subsets of the 

TDS (50 and 102 contaminants) produced mixed results for the five model classes. QUEST and 

linear models exhibited no logical inconsistencies, but ANN, MARS, and CART showed some 

problems. Most dramatic was MARS, which placed contaminants with the very lowest health 

effects and occurrence scores in the List category. Clearly, additional training data was needed to 

overcome these difficulties. No class of model was eliminated on the basis of these findings. 
 

 

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The final TDS (size 202) allowed all of the classes to improve their performance. ANN was 

found to have no logical inconsistencies. Although MARS and CART improved significantly, 

both had some areas of non-monotonicity. This means that there were some cases where an 

increasing attribute score could lead to a decreasing classification for a contaminant. (This 

inconsistency is discussed and displayed graphically in Section 4.4.2.) 

4.2.2 Training after Selected “Outliers” Are Removed From the TDS 

The linear model was most sensitive to selected TDS contaminants. Fortunately, this model 

provided a number of tools for identifying outliers. While other models had the objective of 

minimizing the count of classification errors (or in the case of QUEST, a weighted sum of 

classification errors), the linear model attempted to minimize the deviance between its prediction 

and the average classifications for TDS contaminants. When the other models encountered an 

outlier (for example, a contaminant with very high attribute scores, but a classification of NL), 

they did not attempt to make the correct classification for the outlier because that would have 

meant making other errors for nearby contaminants. Including or not including such an outlier 

had no effect on the outcome. The linear model, in essence, attempted to minimize the squared 

estimation error, so outliers tended to have some influence on the linear model parameters. 

Residuals plots such as Exhibit 21 revealed potentially important outliers for the linear model. 

Exhibit 21 shows the model-estimated versus team classification of one important outlier: a 

contaminant with scores (4, 8, 10, and 10) with a team-average classification of 3.17 (L?) and 

model-estimated value of 3.88 (L). Another contaminant has as large a residual (model = 1.53 

and team = 2.33, both NL?). However, when the model was run first with one and then the other 

contaminant removed, only the first outlier was found to have a marked influence on the overall 

error rate (number of misclassifications and weighted sum of misclassifications). When EPA’s 

subject matter experts were asked about these two contaminants, they agreed that their 

classification for the first contaminant was influenced by their belief that it was a ubiquitous 

inorganic that should probably not be listed. When asked how the model should treat PCCL 

contaminants with such high Severity and occurrence levels, the team agreed that the correct 

decision would probably be to List the contaminant, but that the two tens for occurrence 

suggested that the contaminant was inorganic biasing them towards the lower decision category. 

It was decided to drop this contaminant from training the linear model. Because it had negligible 

influence on the other models, it was included for them. 
 

 

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Exhibit 21. Linear Model-estimated versus Team Average Classification for the TDS

1 1.5 2 2.5 3 3.5 4 

0 

1 

2 

3 

4 

5 

Estimated Decision 

Team Mean Decision (doesn't include perfect 1 = NL and 4 = L) 

The graphical displays discussed in Section 4.4.2 were used as additional checks for outliers. The 

outliers for the linear model were apparent when the training data set was plotted against the 

background display. The inorganic contaminant that was eliminated from linear model training 

was seen to fall “between” two other contaminants that were both assigned to the List category – 

further evidence that its classification of L? may have been inappropriate, at least for the purpose 

of training this model. 

4.2.3 Graphical and Statistical Analyses to Identify Significant Differences in Attribute 

“Weights” Or Influence on Model Performance 

Graphical displays of model outputs (Section 4.4.2) revealed that all of the attributes were 

important. The ANN graph is the only means of studying the ANN rule, but QUEST and the 

linear model provide mathematical expressions that clarify the roles of the four attributes. For 

QUEST, each “node” of the tree involves comparing a weighted sum of attribute scores with a 

threshold. If the threshold is surpassed, then the “right” path is taken, otherwise, the “left” path is 

taken. The QUEST software is capable of using fewer than four attributes, and when trained with 

about half of the 202 TDS contaminants, it sometimes used only three of the four. When the full 

TDS was used, however, all four attributes were used at each of the tree’s seven final nodes. At 

each node, the four attributes can be ranked in order of their model coefficient. Exhibit 22 shows 

the ranking of attributes for the nodes of the final QUEST tree. 
 

 

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Exhibit 22. Relative Weights of Attributes at QUEST Nodes 

(1 = greatest weight, 4 = least weight) 

Node # 1 Potency Severity Prevalence Magnitude N 2 

1 1 2 4 3 202 

2 1 2 4 3 141 

3 2 1 3 4 61 

4 1 3 4 2 52 

5 1 2 4 3 89 

7 1 3 4 2 18 

28 2 1 3 4 23 

1 

 Numbers as assigned by QUEST. 2 

 N = Number of TDS contaminants that are evaluated at the node. All 202 are evaluated at the first node. Of 

these, 141 proceed to node 2, while the remaining 6 1 pass to node 3 (see Appendix E for additional details).
 

 

 

Overall, it appears that Potency carries the most weight, followed by Severity, Magnitude and, 

Prevalence.
 

The linear model assigns a weight to each attribute and the greatest of these is that of Potency, 

followed by Severity, Prevalence, and Magnitude (see also Appendix E). The order of 

Prevalence and Magnitude is the reverse of that found for QUEST. The linear model also 

provides a means of testing the statistical significance of the intercept and four coefficients. 

Because the model accounts for possible censoring, this testing is not as simple as in a leastsquares regression. Two methods were used to approximate the covariance matrix for this model. 

The first is based on the Fisher information (J(model parameters θ)), derived using the likelihood 

function, L(data|θ):
 

J(θ) = - E [d2 ln(L(data|θ)) / dθ2 | θ] 

The second used a Bayesian posterior sample of parameter values. This sample produced a 

covariance matrix that was nearly identical to that derived from the Fisher information, 

suggesting that the likelihood and posterior are very nearly multivariate normal. Hypothesis tests 

could therefore be conducted using the Markov Chain Monte Carlo (MCMC) sample (10,000 

sets of parameter values). Exhibit 23 below shows means, medians, and 95% credible intervals 

for the model parameters. b1 through b4 are the parameters for the four attributes (Potency, 

Severity, Prevalence, and Magnitude, respectively), b0 is an intercept term, and Phi is the 

precision (inverse of the error variance). The 95% intervals reveal that all of the attribute 

parameters are statistically significantly greater than zero. 
 

 

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Exhibit 23. Summary Statistics from MCMC Sample 

Parameter Mean 2.5% Median 97.5% 

b0 -1.674 -1.865 -1.673 -1.488 

b1 0.2410 0.3343 0.241 0.2591 

b2 0.2170 0.2002 0.2169 0.2342 

b3 0.1157 0.1033 0.1157 0.1284 

b4 0.1699 0.1539 0.1699 0.1858 

Phi 14.25 11.44 14.22 17.41 

Based on the MCMC sample, pair wise comparisons of attribute parameters were all found to be 

statistically significant. Separate weights are needed for the two health effects attributes and for 

the two occurrence attributes. 

4.3 Model Performance Testing 

The TDS, Attribute Scoring Protocols, and prototype model test results were linked together in 

an iterative process. Testing of the models in the early stages was impacted by changes and 

refinements in attribute scales, resulting changes in the scores, and changes in the composition of 

the TDS. These changes required iterative reevaluation of the models and resulted in many 

improvements that are part of this final analysis. Refinements in scoring are discussed further in 

Chapter 2 and development of the TDS in Chapter 3. EPA also evaluated the impact of the 

attributes used by the models and the effects of missing data on the performance of the models 

during the various stages of development. 

During early stages of the model testing, the models were run with various sized TDSs. The 

CART and MARS models did not always use all four attributes with some of the smaller TDSs. 

However, all models used all four attributes when trained with the final TDS, consisting of 202 

contaminants. 

Exploratory analysis of the results revealed some additional problems with the CART and 

MARS models. When two contaminants have identical attribute scores for all but one attribute, 

the contaminant with the higher score for that attribute should logically be classified at least as 

high as the contaminant with the lower score. For example, if a contaminant with scores (4, 4, 4, 

4) is assigned to the L? Category, then a contaminant with scores (4, 4, 4, 5) should not be 

assigned lower, to category NL? or NL. Both CART and MARS rules had this type of 

misclassification. Both models did not consistently classify contaminants. Another problem with 

the CART and MARS models was their errors across two categories. Both models did not 

consistently separate the NL? from the L contaminants or separate the L? from the NL 

contaminants. Because of these problems, and because of poor performance with respect to the 

training set decisions, EPA decided not to use these two models to inform PCCL to CCL 

decisions. 

Three models, ANN, QUEST and Linear Regression consistently demonstrated the best 

performance when using the final TDS. Exhibit 24 lists the features of these three models. 
 

 

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Exhibit 24. Features of the Three Preferred Models Based on TDS Test Results 

Features 

Classification Models 

Artificial Neural 

Network 

Classification Tree 

with Linear Nodes 

(QUEST) 

Linear Regression 

Objective Function Minimize count of Minimize count of Maximize 

(to be minimized training set errors training set error likelihood or 

or maximized) loss OR minimize 

error loss 

minimize error loss 

Prediction Rounded average 

subject matter 

expert classification 

Rounded average 

subject matter 

expert classification 

Average subject 

matter expert 

classification (not 

rounded) 

Ranking Rank by Probability Rank by Rank by prediction 

Capability (Probability of List) classification and 

distance from 

discriminant 

(requires postprocessing) 

Transparency of 

Optimization 

Method 

Not transparent Not transparent Simple and 

transparent 

Classification Rule Not clear, but 

classifications 

available for all 

attribute score 

combinations. 

Clear. Complex 

classification tree 

with linear 

inequalities for 

intermediate nodes 

Clear. Simple linear 

function of attribute 

scores. 

Computation < 1 Second < 1 Second (but < 1 Second 

Speed process for deriving 

distances for 

ranking is not part 

of software) 

Software Cost Version used is 

Freeware. 

Freeware No special software 

4.4 Evaluating Classification Differences 

This section describes how the classification models were assessed and compared with respect 

to: 

•	 The number of correct and incorrect classifications for the 202 contaminants in the final 

TDS 

•	 The number of “large” misclassifications (off by more than one category) 

•	 The weighted sum of TDS classification errors 

•	 Ability to identify intermediate classifications 

•	 Consistent behavior (e.g., no decreasing classification as attribute scores increase) 
 

 

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As described in Section 3.3.1, the approach to classifying the TDS contaminants became a fourcategory decision (L, L?, NL?, and NL) to allow the EPA subject matter experts, experienced in 

making list or not list decisions, to identify the decisions that were not strong list or not list 

decisions. Accordingly, quantification of model performance as it compared with the decisions 

of the EPA subject matter experts had to consider a suite of various misclassification outcomes 

(Exhibit 21), such as a consensus decision that a contaminant should be a L?, but the model 

classifying it as a L. However, not all the misclassifications are considered to be equally serious. 

Of the differences, the most substantive misclassification would be placing a strong “List” 

contaminant in the “Not List” category. This might result in missing a key candidate for the 

CCL. To consider the relative seriousness of the different kinds of misclassifications, EPA 

developed the classification error losses in terms of the weights displayed in Exhibit 25. Initially, 

the table had equal weights for all misclassifications and these were adjusted until EPA was 

comfortable that they represented the relative significance of the 12 misclassifications or errors 

that are possible. The most serious error (placing a List contaminant in the Not List category) has 

ten times the weight (i.e., a 10) of the least substantive difference (placing a contaminant one 

category too high, such as placing a List? contaminant in the List category, i.e., a value of 1). 

Exhibit 25. Decision Comparison Matrix; Weight of Differences 

Model Decisions 

Subject Matter Expert Decisions 

Not list Not list? List? List 

Not list  2 5 10 

Not list? 1  2 5 

List? 2 1  2 

List 3 2 1 

The Decision Comparison Matrix and the quantitative weighting of differences were used to 

compare model results to EPA decisions. This was part of the process to minimize the losses and 

cost of the misclassifications. 

The models are tools to help classify and prioritize the contaminants for expert review at the end 

of the CCL process. After applying the models, EPA plans to scrutinize all of the contaminants 

identified as “List,” but likely will spend less evaluation time on those placed in the other 

categories (particularly the “Not List”). As a result, EPA recognized the need to minimize the 

likelihood of classifying “List” contaminants as “Not List” or “Not List?” and applied the 

Decisions Comparison Matrix as a tool in evaluating model output misclassifications. 

4.4.1 Classification Differences Among the Models 

Appendix E describes the classification rules or “solutions” that were generated by the different 

models. These rules perform differently, when compared with the TDS consensus decisions. 

Exhibit 26 summarizes the number of each type of decision by each model compared to the 

subject matter expert consensus decisions and Exhibit 27 summarizes the results and summed 
 

 

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products from the Weighted Loss Value (Exhibit 25). The model input (and output) for ANN, 

CART, MARS, and QUEST were the integers representing the classes (i.e., 4=L, through 1=NL) 

while the Linear model estimated the average classification. When a majority of subject matter 

experts favored one classification for a contaminant, that class was assigned. When the subject 

matter experts were evenly split (for example, if three assigned a contaminant to 1 (NL) and 

three assigned it to 2 (NL?)), an agreement was reached to assign the contaminant to the higher 

of the two categories (2 (NL?) in the case of even split between scenario 1 and 2). In contrast, the 

Linear model predicted the average classification and was trained using average classifications 

for the TDS. For example, if three decision makers assigned a contaminant to 1 and three 

assigned it to 2, the average classification was 1.5. 

Exhibit 26. Summary of Quaternary Model Decisions 

Decision 

Category 

Number of Decisions in Category by Model 

Expert 

Workgroup 

Blinded 

Decision 

ANN CART Linear MARS QUEST 

4 (L) 42 42 27 27 47 55 

3 (L?) 56 55 68 69 38 49 

2 (NL?) 65 65 73 69 81 58 

1 (NL) 39 40 34 37 36 40 

Total 202 202 202 202 202 202 

Exhibit 27. Results of 202 Model Classifications and
 

Weighted Misclassifications
 

Number of 

Classification Weighted Loss 

matching Value 

TDS 

ANN 168 52 

CART 156 84 

Linear 160 72 

MARS 160 67 

QUEST 174 33 

 

 

 

While there are important differences, all the models were able to process the TDS and produce 

classification rules. All five models produced from 79 percent to 86 percent exact matches with 

the consensus decisions. Exhibit 28 provides further details on the predicted classifications for 

each model. Perhaps most important, no model classed any consensus L(4) or L?(3) decision as a 

NL (1). Only CART classed any L(4) candidates (2%) as NL?(2). 

The best performance, by these metrics, was that from the QUEST model, while the lowest 

performance was by CART. The objective of the QUEST model was to minimize the value loss 

of the misclassifications, while the other methods minimized errors with no regard for the 
 

 

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weights shown in Exhibit 25. As a result, QUEST has the lowest loss, and the highest exact 

match rate. Note on Exhibit 28, that QUEST’s misclassifications are all shifted to the “left;” i.e., 

QUEST only predicted 2 of consensus L? decisions would be NL?; and 8 of consensus NL? 

were predicted to be L?. EPA believes this is a more acceptable and conservative difference. 

ANN attempts to maximize the likelihood of correct predictions and simply minimize the 

number of misclassifications (not their weighted value). Its misclassifications are rather equally 

distributed around the exact match categories. The performance of MARS and the Linear model 

look similar, but MARS had the highest value of any model for consensus L? decisions that were 

predicted as NL? (16), a less acceptable difference. 

4.4.2 Logical Evaluation of the Models – Graphical Analysis 

As introduced in Section 3.2.1, the testing of the models included evaluation of the total potential 

“attribute space.” The total “attribute space” for a model that includes four attributes with scores 

from 1 to 10, is 10,000 combinations of possible attribute scores. The graphical analysis of 

model performance looked at how the models generated decisions on the category to which it 

assigned contaminants (L or NL). When applied across the entire attribute space, the 

discriminate surfaces that bound the model’s decisions on the category to which it assigned any 

possible score became apparent. These category boundaries or discriminant surfaces were 

reviewed for consistency through the graphical analysis. Five models (ANN, QUEST, MARS, 

CART, and Linear Regression) developed with the 202 TDS produced classification rules that 

were applied to the 10,000 scores and plotted to evaluate their performance (Exhibits 29 through 

32). 

Exhibit 29 is another example of the graphic tool introduced in Chapter 3, Exhibit 19, to help 

visualize the multi-dimensional space of the CCL classifications. The graphical analysis shows 

five elements of the model results, the four attributes evaluated and the categorical decision (L, 

L?, NL?, and NL) in a single graph. Note in Exhibit 29 that the vertical and horizontal axes show 

two attributes on each axis. The attribute scores for Potency are the large squares across the 

horizontal axis. The corresponding score for Severity for each Potency score is a separate scale 

within each larger square. That is, each Potency square has a range of Severity scores. Similarly 

the Prevalence and Magnitude scores are plotted on the vertical axis with Prevalence along the 

primary axis and Magnitude along the axis imbedded in each Prevalence square. The categorical 

decision assigned to each potential attribute score combination is color coded. Red represents a L 

decision, beige, a L?; light blue represents a NL? and dark blue represents a NL decision. 
 

 

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Exhibit 28. Summary of Individual Quaternary Model Classifications 

(shaded cells are exact match with Expert Decisions) 

Consensus 

Blinded Model Decisions 

Decisions 

ANN 

4 (L) 3 (L?) 2 (NL?) 1 (NL) 

4 (L) 37 5 0 0 

3 (L?) 5 44 7 0 

2 (NL?) 0 6 53 6 

1 (NL) 0 0 5 34 

CART 

4 (L) 3 (L?) 2 (NL?) 1 (NL) 

4 (L) 26 12 4 0 

3 (L?) 1 47 8 0 

2 (NL?) 0 9 53 3 

1 (NL) 0 0 8 31 

Linear 

4 (L) 3 (L?) 2 (NL?) 1 (NL) 

4 (L) 26 16 0 0 

3 (L?) 1 47 8 0 

2 (NL?) 0 6 54 5 

1 (NL) 0 0 7 32 

MARS 

4 (L) 3 (L?) 2 (NL?) 1 (NL) 

4 (L) 37 5 0 0 

3 (L?) 10 30 16 0 

2 (NL?) 0 3 59 3 

1 (NL) 0 0 6 33 

QUEST 

4 (L) 3 (L?) 2 (NL?) 1 (NL) 

4 (L) 42 0 0 0 

3 (L?) 13 41 2 0 

2 (NL?) 0 8 54 3 

1 (NL) 0 0 2 37 
 

 

Exhibit 29. ANN Model Predictions for the Four Attribute Space

(10,000 possible score combinations) 

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The colors represent the classification decision: List = red; List? = beige; Not List? = light blue, and Not List = dark blue. One TDS 

contaminant (Potency = 4, Severity = 8, Prevalence = 5, and Magnitude = 10) is shown in black, though EPA’s decision for that 

contaminant is List (red). This particular contaminant is always shown in contrasting color to help the viewer orient to the details of the 

graph and check the scaling and axes. 

____________________ 1 Expressed in RGB format, dark blue is (5 113 176), light blue is (146 197 222), beige is (244 165 130), and red is (202 0 32). These 

colors were selected using ColorBrewer, by Cynthia A. Brewer of Penn State University. ColorBrewer can be found online at 

www.ColorBrewer.org. 
 

 

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Exhibit 29 plots the results of the ANN models classifications for the 10,000 combinations of 

attribute scores. The patterns clearly show a logical progression from the lower left to upper 

right, progressing from Not list predictions (dark blue) for low attribute scores, through NL? and 

L?, to List classifications for the highest scores, both within each square and across the entire 

matrix. The graphical analysis helped to understand and visualize the logic of the discriminant 

approach of models and to visualize the performance with the TDS. The QUEST model produces 

a very similar graphic result to the ANN model. 

In contrast to Exhibit 29, Exhibit 30 shows the MARS results. The figure shows areas where red 

(L) directly touches light blue (NL?) and where dark blue (NL) touches beige (L?). Both are 

indications that the model was unable to define the intermediary categories. Another problem can 

be seen in the lower right box of the figure, where Potency is 10 and Prevalence is 1. Within that 

box, when magnitude is 1(along the bottom edge of the box), as Severity increases, the decision 

can be seen to go directly from NL? to NL (light blue to dark blue). This unacceptable result also 

occurs for several other combinations of high Potency and low Prevalence. EPA found that these 

results were illogical and unacceptable. Exhibit 31 shows that the univariate CART model 

exhibited similar problems. 

The adapted Linear Regression model, shown in Exhibit 32, presents an interesting variant. As 

noted, the Linear model predicts average classification of contaminants. In other words in 

contrast to ANN or QUEST which predict a classification as an integer of 3 (or L?), the Linear 

model predicts the value from the regression model, such as 3.312 (rounded to 3 = L?), so the 

colors can be displayed more as a continuous variable. The Linear model again displays a very 

logical function across the total attribute space. 

As discussed above, the CART and MARS models exhibited inconsistent categorization of 

contaminants and poor performance in the decision matrix comparisons, while the other three 

models (ANN, Linear, and QUEST) performed very well with respect to TDS error loss, number 

of training set errors, and the logic of the classification model. The linear model was generally 

able to predict EPA average within approximately 0.3 (less than half a category). Hence, 

evaluating ways to apply the model results focused on procedures for utilizing the results from 

the ANN, Linear, and QUEST models. 
 

 

Exhibit 30. MARS Model Predictions for the Four Attribute Space

(10,000 possible score combinations). 

See Exhibit 29 for the key and text for discussion. 

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Exhibit 31. Univariate CART Model Predictions for the Four Attribute Space 

(10,000 possible score combinations) 

See Exhibit 29 for the key and text for discussion. 
 

 

Exhibit 32. Linear Model Predictions for the Four Attribute Space

(10,000 possible score combinations) 

See Exhibit 29 for the key and text for discussion. 

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4.5 Applying Model Results 

From the inception of the development of the CCL classification process, EPA intended to use 

classification models as decision support tools. It was envisioned that, after testing and 

evaluation, a model(s) might be used to process complex data in a consistent, objective, and 

reproducible manner and provide a prioritized listing of candidate contaminants for the last stage 

of the CCL process, an expert review and evaluation. This also would help to focus resources for 

the review and evaluation of potential contaminants. The use of classification models as a tool in 

the CCL process is a new application of such tools. 

Several factors have been considered in assessing how to utilize the model results. After testing, 

EPA determined that three models performed well: the ANN, Linear, and QUEST models. These 

are three different classes of models, with three different mathematical approaches, but all 

provided similar results and logical determinations. Yet the results of each are unique (e.g., 

Exhibits 29 and 32). Therefore, EPA explored ways to combine the results of all three models, to 

capture both agreement among models and unique results. Two straight forward approaches 

looked most useful and were applied: a simple additive approach, and a collective rank-order 

approach. 

4.5.1 Additive Model Results 

The first step in combining the results of the three models was to simply add the results of their 

classifications for each contaminant. A tabulation of all contaminants (in the TDS) was prepared 

with their predicted classification from the models. Recall, the model output is as a class 

(number), with 4 equaling L through 1 equaling NL. The Linear model output was rounded to its 

integer class for this approach). Then the 3 results were simply added. This resulted in 10 “bins” 

or classes, ranging from 3 (all three models classed the contaminant as a 1) to 12 (all three 

models classed the contaminant as a 4). Hence, a contaminant with an additive score of 11, had 

two models class it as 4 and one model class it as a 3, totaling 11. A comparison of the sum of 

the three models to the TDS workgroup Decisions is shown in Exhibit 33. 

Exhibit 33, shows some important features of the additive process. For 142 of the 202 

contaminants, the three models were unanimous and in agreement with the TDS. When 

reviewing these analyses EPA noted that every contaminant subject matter experts classed as 

List (by consensus) was predicted as a List by at least one model. The models do move some 

NL? into a strong L? position, but only 2 of the L? contaminants were placed into the NL? 

category. The areas where the models differ in outcome can provide a place to focus some 

review during the development of future CCLs. 

4.5.2 Additive Rank Order Results 

EPA also tested a different approach from the 10 additive classes. A simple method to provide a 

more continuous rank-order for each model was also developed. The output for each model was 

used to produce a rank-ordering for that model; ordering from highest (an L candidate) as 

number one, to lowest (a NL) as number 202 for the TDS. Once the ranks for a model were 

ordered, the contaminants were simply assigned a number from 1 to 202 (high to low). After this 

was done for all three models, the rank numbers were added (resulting in a range from 3 to 606) 

divided by 3 (just to stay on the 202 scale), and then reordered by their composite ranks. 
 

 

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Exhibit 33. Summary Compari

Distribution of 

Sum 3 Model Results 

All 3 = 4 (L) 12 

11 

10 

All 3 = 3 (L?) 9 

8 

7 

All 3 = 2 (NL?) 6 

5 

4 

All 3 = 1 (NL) 3 

Sum 

son of the Sum of the 3 Model Decisions to the
 

EPA Blinded (TDS) Decisions
 

Consensus Blinded Decision 

4 (L) 3 (L?) 2 (NL?) 1 (NL) 

26 1 

11 4 

5 8 

35 6 

1 

5 2 

2 49 2 

4 3 

2 2 

2 32 

42 56 65 39 

Shaded cells are unanimous model decisions

using all models. The analysis reinforced som

applications. 

 that match with the TDS. These analyses were also conducted 

e of the problems discussed for the CART and MARS 

 

 

 

As part of the unique input of the three models, each model produces different output with which 

to develop its own prediction and a rank-order. The Linear Regression model as applied, 

predicted the outcome as a continuous variable by solving the regression equation (e.g., 3.312), 

and these values were simply used to rank-order. ANN produces a probability of a contaminant 

being a 4. So, for ANN, the probabilities for each contaminant were used for the rank-ordering. 

QUEST does require some processing after the model produces classification predictions to 

produce a rank order. For QUEST, the distance from the lower discriminant surface was 

computed. The contaminants were then rank-ordered within a classification group (i.e., ranked 

within the L? group), then a composite was compiled. QUEST, as a classification decision or 

regression tree, produces more ties than the other models, but it still produces enough of a 

continuum that it did not present a problem. 

The composite provides a nearly continuous rank-ordered list that can further help to prioritize 

the analysis for the expert review. Combining the additive results and the rank ordering could 

also be useful. Knowing which contaminants get unanimous 4s and 1s, or identifying 

contaminants that stand out as anomalies in one model was useful in the review of the model 

output. Having the rank-ordering within the group that included an L? decision, for example, was 

useful for prioritizing additional evaluation. 
 

 

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5.0 MODEL OUTCOME AND POST MODEL EVALUATION PROCESS 

The preceding chapters have described the process that was developed for selecting the CCL 

from the PCCL. The companion document, Final CCL 3 Chemicals: Screening to a PCCL 

(USEPA, 2009b), describes the approach that was used for screening and selecting the PCCL 

from the Universe of chemicals. Once the PCCL screening was executed, the Attribute Scoring 

Protocols finalized, and the models trained, all of the PCCL chemicals were scored for their 

attributes and run through the models. This chapter describes the results from the modeling and 

the processes EPA used in evaluating the model output before selecting the CCL 3. 

EPA evaluated the model output and formulated several post-model refinements that were added 

to the CCL selection process, including an approach for considering the certainty reflected in the 

differing data elements. The post-model analyses are also described in this Chapter. 

5.1 PCCL Characterization and Model Results 

The screening process, described in “Final CCL 3 Chemicals: Screening to a PCCL” (USEPA, 

2009b), selected the chemicals for the PCCL. The attributes for these chemicals were scored 

using the procedures presented in Chapter 2 and evaluated by the three models described in 

Chapter 4. Exhibit 34 illustrates the results of the model output for the PCCL contaminants 

developed for the Draft CCL 3. These results show the distribution of the different types of data 

and information EPA used to evaluate occurrence and potential occurrence8

. The PCCL 

consisted of chemicals with variable health effects data, ranging from RfDs to LD50, and 

occurrence data, ranging from measured water concentration data from PWSs to production 

volume data. 

Exhibit 34. Model Results for the PCCL Chemicals 

3 -

Models 

Decision 

% of 

PCCL 

Total 

# 

PCCL 

Finished 

or 

Ambient 

Water 

Release Production 

L 9% 44 3 24 17 

L-L? 12% 58 9 29 20 

L? 33% 163 26 64 73 

NL?-L? 6% 30 6 11 13 

NL? 28% 139 29 28 82 

NL?-NL 4% 20 7 9 4 

NL 9% 46 21 7 18 

N(all) 100% 500 101 172 227 

 

 

 

As described in Chapter 4, three models (ANN, Quest, and Linear) were used in classifying the 

PCCL contaminants. EPA used an additive process to combine the results of all three models. 

8 The screening of the CCL 3 Universe, including processing with supplemental data during the nominations 

process, resulted in 532 chemical contaminants for the PCCL. These chemicals were scrutinized as part of the 

classification and modeling process. Some of the PCCL chemicals had limited data available for scoring and could 

not be run through the models process. The 32 contaminants that had limited data remain on the PCCL. They are 

identified in Appendix G. Exhibit 34 recaps the model output for the 500 chemicals that were scored and processed. 
 

 

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The bolded decision category (i.e. L, L?, NL?, NL) in Exhibit 34 signifies that all of the models 

were in 100% agreement with that listing decision. The other categories (e.g., NL?-NL) represent 

varied agreement where one or two of the models choose one listing option and one or two 

models chose a different option. None of the models categorized a contaminant in a category 

more than one category higher or lower than the other models. That is, no contaminants were 

categorized as an “L” by one model and as an “NL?” by another model, or vice versa. The 

models categorized approximately 1⁄2 of the chemicals on the PCCL as L? or above. When 

analyzed by data type, the majority of chemicals in the List category had LD50 data for health 

effects. This was a concern and became an important issue for consideration in the post-model 

evaluation process. 

5.2 Evaluation of the Modeling Output 

As part of the last stage in the CCL classification process, the model output was reviewed by 

internal EPA experts. This step involved: 

• a more detailed review of the data used, 

• a review of supplemental data, and 

• deliberations on how the model data should be used to produce a draft proposal for a 

CCL. 

 

Specifically, the function of the team was to critically compare the results from the model to the 

information collected for the individual chemicals, and identify any concerns with the model 

output. This exercise was conducted for a cross section of the model outcomes and their 

associated contaminants. 

An Evaluation Team was comprised of internal EPA experts representing scientists, engineers, 

toxicologists, and environmental protection specialists from the OW, Office of Research and 

Development, Office of Children’s Health, and Office of Pesticide Programs. The Evaluation 

Team met on a weekly basis for approximately 8 weeks to discuss the evaluation of modeling 

results. 

5.2.1 Procedure 

Prior to the initiation of the evaluation effort, all Evaluation Team members received background 

descriptions of the CCL process for chemicals (Chapters 1-4 of this document), Attribute Scoring 

Protocols, and evaluation work sheets. A spread sheet with the attribute scores, the data that 

supported the scores, and the model output for each of the chemicals selected for the first review 

session was also included in the package. An initiation meeting was held to familiarize the 

participants with the contents of their evaluation package and discuss the approach that would be 

followed in evaluating the model output for individual contaminants. 

Participants on the Evaluation Team received a set of contaminants and supplemental data 

dossiers for evaluation. The completed evaluation sheets were submitted so that the results could 

be compiled for discussion. The evaluation sheets allowed the participants to: 
 

 

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•	 Comment on the model input data for each attribute, 

•	 Provide a statement on their level of confidence in the data underlying each attribute 

score, 

•	 Express agreement or disagreement with the model output, 

•	 Indicate their degree of confidence in the model decision, and 

•	 Provide an explanation for their agreement of lack of agreement with the model decision. 

Following submission of the evaluation results for each set of contaminants, the Evaluation 

Team discussed the outcome of the evaluation, concentrating first on those contaminants with the 

greatest differences among the reviewers. These discussions identified the issues and steps 

described in the following sections of this chapter. The Evaluation team reviewed a subset of 129 

chemicals from the PCCL. The contaminants were divided into groups as follows: 

•	 Contaminants with finished and/or ambient water data, 

•	 Contaminants with release data (pesticide applications and/or TRI), and 

•	 Contaminants with production data. 

The team evaluated all contaminants with finished and/or ambient water data and a randomly 

chosen subset of the contaminants with release or production data. The identities of the 

contaminants were blinded for the review. This was done so that the team would focus their 

review on the data for a contaminant and not its name. The identity of all contaminants was 

revealed when the team discussed the evaluation results. 

5.2.2 Evaluation Results 

Discussion of the model results raised issues that are important to the selection process for 

CCL 3 and subsequent CCLs. The evaluators represented a variety of disciplines and contributed 

important perspectives reflecting their field of specialization. Below are some of the important 

issues that were raised by evaluators: 

•	 The ratio between the health reference value and the concentrations observed in finished 

and/or ambient water is an important relationship that is not entirely captured by the four 

attribute scores. When finished and/or ambient water data were available, this ratio was 

most often the reason for not agreeing with the model output. For example, the model 

may have classified a chemical as an L?, but when the health value and concentration 

data were directly compared, the outcome indicated that occurrence was one or more 

orders of magnitude below the health-based benchmark. In this situation, the evaluators 

usually disagreed with the models decision. 

•	 Confidence in the data elements used for attribute scoring varied widely among the 

PCCL contaminants. Evaluators noted that there was a considerable difference in the 

weight-of-evidence for the differing types of data used to score PCCL contaminants. 

Although the scores used a hierarchy in selecting the data elements that best represented 

health effects and occurrence, the most highly ranked data element was not equivalent for 

every chemical. Individual chemicals used different combinations of data as input for the 

models. The type of data elements used to represent the occurrence and health effects 

Page 60 of 70 
 

 

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became a subject of discussion for the Evaluation Team. Some contaminants had recent 

UCMR monitoring data combined with an Office of Pesticide Programs (OPP) RfD and 

others had TRI release data combined with an LD50. For some chemicals, the only 

available data came from an LD50 combined with the number of pounds produced per 

year and environmental fate properties. The evaluators were more comfortable with the 

model decisions based on strong supporting data (i.e., RfD and finished water 

occurrence) than on those based on weak data sets (i.e., LD50 and production data). 

•	 Reviewers believed it was important that the occurrence and health values represent the 

same form of the chemical. This is particularly important for nonmetals where the 

common inorganic form of the element is a complex ion (i.e. phosphate) and not the 

element (i.e. phosphorous). This is also important for metals (i.e vanadium) where the 

occurrence data represent ions in solution that may have been paired with a toxicity value 

for the free metal. 

•	 Toxicity data from National Cancer Institute/National Toxicity Program bioassays were 

incorporated into the Universe for a number of contaminants that were positive for 

tumors, and were tested by way of the inhalation route of exposure. Some of these 

contaminants were screened to the PCCL on the basis of their qualitative cancer findings. 

They were scored for Potency and Severity based on slope factors that had been derived 

for the oral route of exposure, but based on the inhalation data without the use of 

Physiologically Based Pharmacokinetic (PBPK) modeling. Some of these very volatile 

contaminants received L or L? model designations. Reviewers questioned whether 

toxicity data from inhalation studies should be used for scoring cancer Potency. 

Therefore, only cancer slope factors that were derived using PBPK modeling for cross 

route extrapolation were used to score chemicals. Inhalation data were not used for noncancer endpoints. 

•	 Due to the risk assessment policy differences between agencies, the hierarchy for scoring 

Potency and Severity considered the agency that established the value (described in 

Chapter 2 and listed in Appendix A). However, some reviewers questioned whether the 

date of the assessment rather than the Agency conducting the assessment should be the 

basis for the hierarchy. 

•	 Prevalence and Magnitude were given the lowest possible scores (“1”) when a 

contaminant had been monitored but there were no detections. Since the detection level 

for a few chemicals was above the health-based value, some reviewers questioned 

whether this was appropriate. They suggested that it might be better to use the detection 

limit as the basis of the Magnitude attribute score. 

•	 UCMR 1 screening studies monitored a small number of statistically selected sites (300). 

There were cases where there were no finished water detections in the screening surveys, 

but the same contaminant had been detected in ambient water by USGS. Reviewers 

questioned the placement of finished water above ambient water in the hierarchy in these 

cases. 
 

 

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Exhibit 35. Results of the Model Output Evaluation (Total = 129 chemicals) 

Finished/Ambient 

Water Grouping 

Release 

Grouping 

Production 

Grouping 

Number of Contaminants 89 28 12 

Agreement with model outcome (>50%) 96% 89% 67% 

% where an outcome higher than the 

model was recommended 2% 0% 0% 

% where an outcome lower than the model 

was recommended 2% 11% 33% 

% high confidence decisions (avg.) 36% 16% 7% 

% medium confidence decisions (avg.) 49% 31% 17% 

% low confidence decisions (avg.) 15% 52% 76% 

•	 A number of disinfection byproducts (DBPs) had occurrence data based on production or 

release, while some had no occurrence data. Production and release data do not 

adequately represent the potential occurrence of DBPs and byproducts of other treatment 

processes in finished water. 

•	 Reviewers were uniform in believing that contaminants that had a Potency score based on 

an LD50 value and a Severity score of 9 (death), should be returned to the Universe 

independent of their other attribute scores. 

The quantitative results of the model output evaluation are summarized in Exhibit 35 

For Exhibit 35, agreement with the model outcome by a majority of the Evaluation Team 

constitutes agreement. Appendix F lists the chemicals reviewed by the Team and the percentage 

of the team agreeing with the model outcome for the individual chemicals. 

5.3 Post-Model Adjustments to the Process 

Based upon issues identified by the Evaluation Team comments, several post-model refinements 

were added by EPA to the CCL process. The post-model refinements changed the listing status 

of some of the chemicals as candidates for CCL 3. For example, EPA evaluated the UCMR 

screening studies to determine the adequacy of the analytical method for contaminants with no 

detections when ambient water occurrence data was available. In these cases the Agency opted to 

use the ambient water data and included contaminants on the CCL. The post-model adjustments 

that were incorporated are discussed in the following sections. 

EPA re-evaluated health effects data to ensure that toxicological data matched the various forms 

and valences of contaminants and that those data used appropriate cross-route extrapolation 

methods to develop values from different exposure routes (i.e., inhalation to ingestion). The 

Agency did not change the health effects hierarchy to use the most recent data from any source 

rather than best available data from the most suitable Agency. The protocols and established 

hierarchies ensured that the data used at each step and were applied uniformly for all 

contaminants. The hierarchy also provides a transparent data driven approach that allows 
 

 

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stakeholders and the public to uniformly understand the assumptions and processes that were 

applied in the selection of data for individual contaminants. 

Issues raised by the Evaluation Team may have been addressed by one of the post-model 

adjustments and processes. For example, several of the scoring or hierarchical issues identified 

were addressed by implementing the Health-Concentration Ratio. This process addressed 

concerns about the occurrence data hierarchy, magnitude scores based on detection rather than 

the method reporting limit, and using the most relevant data to make listing decisions. 

5.3.1 Using Supplemental Sources to Identify the Data Most Relevant to Drinking Water 

One issue identified by the Evaluation Team was that scoring should be based on the data most 

relevant to exposure from drinking water. For example, DBPs were included in the Universe and 

many were brought forward to the PCCL. The data used to score these contaminants for 

occurrence should be based on their occurrence in drinking water at PWSs, not ancillary data that 

may be available such as release or production volume. There are DBP data from the Information 

Collection Rule monitoring and supplemental studies identified in the CCL Nominations process. 

These data had not originally been included in the data used for scoring Prevalence and 

Magnitude. As part of the post-model processing EPA retrieved the data, scored the chemicals, 

and ran the models using the supplemental data 

5.3.2 Calculation of a Health Effect-Concentration Ratio for Contaminants with Water 

Data 

The models classified chemicals using scores for the four attributes. The Evaluation Team 

recognized that the relationship between Potency and Magnitude was important when deciding 

whether or not to list a chemical. Therefore EPA calculated the ratio between the health-based 

value and the 90th percentile concentration in finished or ambient water as a post-model process 

to select contaminants for the CCL 3. EPA also sought models to predict water concentrations 

for contaminants that did not have direct measurements in water sources to calculate this ratio. 

EPA used the health effect-concentration ratio as key criterion for listing a contaminant on the 

CCL 3if this value was less than or equal to 10. 

5.3.2.1 Developing a Health Reference Level (HRL) 

To calculate the health effect-concentration ratio, the data that provided the Potency score were 

used to calculate the HRL benchmark using a process similar to the one the Agency has used for 

Regulatory Determination. For a carcinogen, the HRL is the one-in-a-million cancer risk 

expressed as a drinking water concentration. For non-carcinogens, the HRL is equivalent to the 

lifetime health advisory value. The lifetime health advisory value is obtained by multiplying the 

RfD times 70 kg, dividing by a water intake of 2 L/day and multiplying by a 20% relative source 

contribution (unless there are data to suggest that the 20% is inappropriate). 

Determining the HRL for chemicals where the Potency value was the NOAEL, LOAEL, or LD50 

value from an individual study, required application of an uncertainty factor to adjust the toxicity 

value to an RfD approximation. In these cases, the uncertainty factor was based on the difference 
 

 

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in the modal values from the log-based data distributions used to develop the Potency scoring 

equations (see Chapter 2). The uncertainty factors applied are as follows: 

• NOAEL = 1,000 

• LOAEL = 3,000 

• LD50 = 100,000 

The NOAEL and LD50 uncertainties were derived from the difference in the constant for the noncancer Potency scoring equation (Exhibit 4). For a NOAEL, the difference is 3 (7 – 4 = 3) or 

1,000 since the Potency equation is log based. The difference for an LD50 is 5 (7-2 = 5) or 

100,000. The uncertainty factor (3,000) chosen for the LOAEL is a half log greater than that for 

the NOAEL, in recognition that the LOAEL is a level that causes effects rather than no effects. 

Exhibit 36 shows the formulae used (including the uncertainty factors), for the CCL 3 program, 

to calculate HRLs from the various Potency data elements. The formulae calculate the HRL as a 

mg/L equivalent, in turn the HRL was converted to μg/L to compare with the CCL 3 water data. 

Exhibit 36. Formulae used in the CCL 3 Process to Calculate 

Health Reference Levels (HRLs) from the CCL 3 Potency 

Data Elements. 

The formulae identify the uncertainty factors (UF) applied for the 

CCL 3. The HRLs are in mg/L. They were further converted to ug/L 

for comparison with CCL 3 water data. BW = body weight; RSC = 

relative source contribution. 

Non-Cancer Equations 

HRL, mg/L = RfD (mg/kg/day) x BW (70 kg ) x RSC (0.2) 

2 L/day 

HRL, mg/L = NOAEL (mg/kg/day) x BW (70 kg ) x RSC (0.2) 

2 L/day x UF (1,000) 

HRL, mg/L = LOAEL (mg/kg/day) x BW (70 kg ) x RSC (0.2) 

2 L/day x UF (3,000) 

HRL, mg/L = LD50 (mg/kg) ) x BW (70 kg ) x RSC (0.2) 

2 L/day x UF (100,000) 

Cancer Equations 

HRL, mg/L = Risk (10-6) x BW (70 kg) 

Slope Factor x 2 L/day 

HRL, mg/L = 10-4 Cancer Risk (mg/L) x 0.01 
 

 

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5.3.2.2 Developing a HRL – Concentration Ratio 

EPA determined that if the measured or modeled concentration of contaminants was equal to or 

greater than on tenth of the health reference level then the contaminant should be included on the 

CCL 3. EPA selected the 90th percentile (of detections) water concentration as the point of 

comparison for the ratio, rather than the mean or median. The CCL list is designed to identify 

contaminants that may benefit from a Health Advisory, even if they do not merit a positive 

regulatory determination. The 90th percentile concentration level was used as a public health 

protective benchmark that may identify a possible need for a health advisory for areas of the 

country that may have higher concentrations in drinking water than others. If a 90th percentile 

concentration level was not available the Agency used the maximum or the next highest 

percentile (i.e., 95th or 99th percentile) reported value. 

The ratio of the heath-value to the 90th percentile concentration detected in water (either ambient 

or finished) was calculated for all contaminants with water data. If the ratio was 10 or less the 

contaminant was selected for consideration for the CCL 3. If the ratio was greater than 10, the 

contaminant was eliminated from consideration for CCL 3 and remains on the PCCL. For 

chemicals that had been monitored but not detected, and for chemicals that were detected in 

ambient waters but not finished water, analytical method detection limits were compared to the 

HRL to ensure that the detection accounted for the health effects. Consideration was also given 

to whether the ambient water data suggested that the UCMR 1 screening might have been too 

limited to identify the contaminant in areas where it might pose a problem. For contaminants that 

had limited finished water data, but more robust ambient water monitoring data, the ambient 

water concentration was used to develop the ratio. The contaminant information sheets note 

which data were used to develop the ratio and are available for all PCCL contaminants in the 

docket at www.regulations.gov. 

5.3.2.3 Developing a Ratio for Contaminants Without Concentration Data 

EPA used modeled data for pesticides when concentration data were not available. The modeled 

concentrations of pesticides in water are included in the OPP registration and re-registration 

evaluation documentation, but they are not readily available in a form that could be used for the 

Universe database. The modeled data predicts environmental concentrations in ground water and 

surface water using a standardized approach for pesticides. 

For pesticides, the modeled data from OPP were compared with the health reference level. As 

part of the pesticide registration process, EPA calculates an estimated environmental 

concentration (EEC) in water or estimated drinking water concentration (EDWC) depending on 

the year the last assessment was completed. Both the EEC and EDWC are derived from models 

that estimate the pesticide's concentration in an index reservoir used for drinking water. OPP 

used the PRZM-EXAMS model for surface water. Ground water concentrations are derived 

using the SCI-GROW regression model to represent exposures in shallow ground water. Both 

the EEC and the EDWC are equivalent. The modeled EEC values allowed EPA to calculate the 

HRL/EEC or EDWC ratio for pesticides and/or their degradates. Pesticides with HRL/EEC ratios 

of 10 and lower were selected for the draft CCL 3. 
 

 

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5.3.3 Grouping Contaminants based on the Certainty or Relevance of the Supporting Data 

Data certainty was not directly factored into the development of the attribute scoring protocols, 

but was indirectly factored into the protocols through the use of the hierarchies of the data used 

for health effects and occurrence (Chapter 2). In the evaluation of the model output, data 

certainty was an important factor for the Evaluation Team. In cases where the model output 

listed a chemical with well developed data from high in the hierarchy (e.g. IRIS RfD, 

UCMR/NAWQA concentration), the Evaluation Team typically agreed with the model decision. 

The Evaluation Team confidence ranking for model decisions based on these types of data was 

generally high while confidence for less developed or preliminary data from the hierarchy was 

generally lower (see Exhibit 35). Accordingly, as part of the post-model evaluation process, EPA 

tried various approaches for addressing the certainty issue. 

Initially, EPA attempted to develop numeric certainty scores for each data element, but decided 

not to use this approach because the certainty scores could not be calibrated due to the 

subjectivity in assigning the numeric values. For example, it would be difficult to justify that a 

chemical evaluated by environmental release data should be assigned a certainty score of 6, 

while a chemical evaluated by production volume should be assigned a certainty score of 10 

versus 9. Therefore, EPA placed tags on the chemicals that characterize the certainty. The 

chemicals were tagged as high, medium and low certainty based on the combinations of data 

elements that were used to score the attributes for health effects and occurrence. The certainty 

tags are not calibrated measures of certainty. They were developed to express the relative 

certainty associated with the data elements that were used to score a chemical’s attributes. The 

certainty rankings assigned to the combinations of individual attribute data elements are listed 

below: 

High Certainty: 

Finished Water + RfD/ CSF, NOAEL or LOAEL 

Ambient Water + RfD/CSF, NOAEL 

Medium Certainty: 

Ambient Water + LOAEL
 

Release/Application + RfD, NOAEL, LOAEL
 

Production + RfD
 

Low Certainty: 

Health effects based on LD50
 

Occurrence based on production values
 

The high certainty bin consisted of chemicals that had been scored based on the most relevant 

data for occurrence in water and with the richest data for health effects. Such contaminants are 

expected to be good candidates for regulatory determination with minimal research needs. 

Examples of chemicals in the high bin include chemicals with reference doses and measured 

water concentration data. The medium bin consists of chemicals that need further occurrence 

and/or health effects research. These include chemicals that may have well studied health effects 

data but may need additional occurrence data (e.g. chemicals with release data but, no measured 

water occurrence data). The low certainty bin consists of chemicals that need extensive health 
 

 

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effects and occurrence research needs that may take longer than the life cycle of a CCL. 

Examples include chemicals with LD50 and/or production volume data. While the CCL consists 

both of chemicals that may provide sufficient data to support regulatory determinations and 

chemicals that are of concern and need additional drinking water research EPA found that LD50 

and production volume data are not sufficient data for a contaminant to be included on the CCL. 

EPA evaluated contaminants from each bin to determine if the best available data for that 

contaminant was sufficient for listing. 

5.3.4 LD50 Values with Limited Documentation 

Following the advice from the Evaluation Team, Severity scores based on death from LD50 

studies were subject to additional research to identify supplemental health effects information. If 

no other information were available the contaminants were removed from the modeled PCCL 

results. (This decision applies to contaminants where no critical endpoint other than death was 

specified in the source of the LD50 data.) These contaminants were removed from consideration 

for the CCL. None of the chemicals on the PCCL with LD50 derived health attributes scores had 

ambient or finished water data. 

5.4 Selecting the Draft CCL 3 

The chemicals for the draft CCL 3 were selected based on the processes previously described in 

this document and the other cited support documents, the Final CCL 3 Chemicals: Identifying 

the Universe (EPA, 2009a), and the Final CCL 3 Chemicals: Screening to a PCCL (EPA, 

2009b). The Agency noted from which of the three uncertainty bins, described in Section 5.3.3, 

contaminants were selected. In selecting contaminants for the draft CCL 3, EPA used the post 

model criteria (described in Section 5.3) for the HRL/Concentration ratio and the certainty bins. 

EPA identified four groups of on the draft CCL 3. 

•	 36 chemicals in the high certainty bin, which have finished water data and an 

HRL/concentration ratio of ≤ 10.
	

•	 24 pesticides in the medium certainty bin, which have modeled surface and/or ground 

water data that yielded a HRL/concentration ratio of ≤ 10 

•	 27 pesticides and chemicals in the medium certainty bin, which have release data that 

gave modeled L or L? rankings 

•	 8 chemicals were initially in the low certainty bin. These contaminants were nominated 

and evaluated with supplemental information that was submitted or evaluated by EPA. 

 

No chemicals with only LD50 and production data were selected for the CCL. These chemicals 

will be considered for future CCLs. 

Subsequent to placement on the draft CCL 3, the list was subject to review by a panel of 

qualified external experts and stakeholders. Stakeholder input was considered in determining 

which chemicals from among a preliminary CCL 3 grouping were retained for the Draft CCL 3. 

A summary of this review is available in the docket at www.regulations.gov. The draft CCL 3 

was published on February 21, 2008, and included 93 chemicals or chemical groups and 11 

microbiological contaminants. 
 

 

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5.5 Selecting the Final CCL3 

EPA provided information on the process, the draft list, and sought comment on its 

efforts to expand and strengthen the underlying CCL listing process, the draft list, and EPA's 

efforts to improve the contaminant selection process for future CCLs. 

EPA received comments from 177 individuals or organizations on the draft CCL 3. Commenters 

identified several issues on the draft CCL 3 and the process used to select contaminants for the 

list. Commenters also provided information and recommendations for the Agency to consider as 

it finalized the CCL 3. The Agency has provided responses to individual comments in the 

“Comment Response Document for the Third Drinking Water Contaminant Candidate List 

(Categorized Public Comments)” document that is available in the regulatory docket at 

www.regulations.gov (USEPA 2009c). 

The EPA SAB and its Drinking Water Committee also reviewed the draft CCL 3 during 2008, 

and provided an advisory to the EPA Administrator on January 29, 2009. EPA staff met with the 

SAB to provide an overview of the draft CCL 3, to answer questions from the Drinking Water 

Committee, and to clarify questions from the full SAB. The Agency also participated in 

teleconferences with SAB during the development of the “SAB Advisory on EPA's Draft Third 

Drinking Water Contaminant Candidate List (CCL 3)” (USEPA 2009e). 

EPA evaluated all the data and information on chemical contaminants provided by commenters 

and collected by the Agency after the draft CCL 3 was published. EPA used the same process 

described in the draft CCL 3 notice (73 FR 9628, USEPA 2008) and other supporting documents 

to evaluate contaminants for which data became available after the publication of the draft 

CCL 3. 

The Agency added contaminants to the Universe, adjusted the contaminants that passed through 

to the PCCL based on new data and reevaluated the PCCL using the protocols described in this 

document. 

The 106 chemicals included on the final CCL 3 included: 

•	 38 chemicals in the high certainty bin 

•	 23 pesticides chemicals in the medium certainty with modeled occurrence data with an 

HRL/concentration ratio ≤ 10 

•	 26 pesticides and chemicals in the medium bin which have application or release data and 

health effects data that resulted in a L, L-L?, or L? classification. 

•	 19 chemicals that were initially in the low or medium certainty bin which EPA or 

commenters identified supplementary data that resulted in an HRL/concentration ratio or 

model classification to be included on the final CCL 3. 

5.6 Summary 

The CCL 3 and the process EPA used to select contaminants was developed and tested by the 

Agency to meet the Safe Drinking Water Act requirements and address recommendations and 
 

 

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advice from the NRC (2001) and NDWAC (2004). The Agency has developed a process and 

final CCL 3 that: 

•	 Considers of a broad Universe of contaminants 

•	 Relies on best available science and information to inform the process 

•	 Evaluates the known or potential health effects and occurrence in screening the 

Universe to a PCCL 

•	 Uses a set of contaminant attributes and prototype classification algorithms as decision 

support tools in selecting candidates for the CCL from the PCCL 

•	 Provides an opportunity for nominations and expert judgment. 

The first application of the CCL 3 process accomplished many of the specific recommendations 

from NRC and NDWAC. During the development of CCL 3, the Agency identified areas for 

improvement that can be implemented in the selection of CCL 4 and later CCLs. 

6.0 REFERENCES 

Fetter, C. W. 1994. Applied Hydrogeology, 3rd Edition, Macmillan College Publishing Co. New 

York. 

Lyman, W. J., Reehl, W. F., and Rosenblatt, D. H. 1990. Handbook of Chemical Property 

Estimation Methods, American Chemical Society, Washington, DC. 

National Drinking Water Advisory Council (NDWAC). 2004. National Drinking Water 

Advisory Council Report on the CCL Classification Process to the U. S. Environmental 

Protection Agency, May 19, 2004. 

National Research Council (NRC). 2001. Classifying Drinking Water Contaminants for 

Regulatory Consideration. National Academy Press, Washington DC. 

NIST. 2006. NIST/SEMATECH e-Handbook of Statistical Methods. Available on the internet at: 

http://www.itl.nist.gov/div898/handbook/, (used on May 3, 2007). 

USEPA. 2004. Office for Water. Drinking Water Standards and Health Advisories, EPA 822-R04-005 Washington, DC. Winter 2004. 

USEPA. 2008. Drinking Water Contaminant Candidate List 3 – Draft Notice. Federal Register. 

Vol. 72. No. 35. p.9628. February 21, 2008. 

USEPA. 2009a. Final Contaminant Candidate List 3 Chemicals: Identifying the Universe. EPA 

815-R-09-006. August 2009. 

USEPA. 2009b. Final Contaminant Candidate List 3 Chemicals: Screening to a PCCL. EPA 815-

R-09-007. August 2009. 

USEPA. 2009c. Final Comment Response Document for the Third Drinking Water Contaminant 

Candidate List (Categorized Public Comments). EPA 815-R-09-010. August 2009. 

USEPA. 2009d. Summary of Nominations for the Third Contaminant Candidate List. EPA-815-

R-09-011. August 2009. 

USEPA. 2009e. SAB Advisory on EPA’s Draft Third Drinking Water Contaminant Candidate 

List (CCL 3). EPA-SAB-09-011. January 2009. 
 

 

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7.0 APPENDICES
 
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Appendix A. Attribute Scoring Protocols 

This section provides scoring protocols for the health effects attributes of Potency and Severity as well as 

the Occurrence attributes, Magnitude and Prevalence. 

A.1 Potency Scoring Protocol 

This section describes the process for assigning a numerical score for the Potency attribute. 

Protocol for Potency Scoring 

Step One: Open the spreadsheet for Potency and Severity Scoring (a sample of this spreadsheet is shown 

in Exhibit A.1) and is an alternative to using the computer version of the spread sheet. 

Step Two: Enter the name of the chemical in the column labeled contaminant. 

Step Three: Identify and score highest-ranked non-cancer data element for potency using the following 

hierarchy of values: 

Reference Dose (RfD) or equivalent > No-Observed-Adverse-Effect Level (NOAEL) that is 

lower than the lowest LOAEL > Lowest-Observed-Adverse-Effect Level (LOAEL) > Toxic DoseLO 

(TDL0- RTECS) > Lethal dose (LD50) 

•	 Measured > Modeled 

 

For RfDs (or equivalent) only: 

•	 EPA RfD > ATSDR Minimal Risk Level (MRL) (Chronic> Intermediate >Acute) > 

RAISHE RfD > Cal EPA Public Health Goal (PHG)

a > TDIs from WHO/EU/Health 

Canada > UL from IOM 

•	 Office of Pesticide Programs (OPP) > IRIS for Pesticides 

Step Four: Enter the selected quantitative measure of non-cancer potency into the appropriate column of 

the spread sheet. Make sure that the units are in mg/kg/day. (The spreadsheet formula produces a score in 

a corresponding column for the data element on the right side of the sheet.) 

Step Five: Select a measure for cancer potency if one is available. The preferable measure will be the 10-4 

risk concentration in drinking water in mg/L. If the risk is expressed at levels other than 10-4, convert the 

value to the target risk (10-4). If the cancer potency measure is the slope factor, calculate the 10-4 risk 

concentration using the following equation: 

10-4 Risk concentration =	 0.0001 x 35 kg/day/L 

 Slope Factor (mg/kg/day)-1 

 

a The California PHG will have to be converted from mg/L to a dose by multiplying it by the [Drinking Water Intake 

(L) ÷ (the body weight (kg) x Relative Source Contribution)]. 

A-1 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Step Six: In a case where the entered potency value is a LD50 value that is reported as greater than a 

particular dose, or as a NOAEL with no LOAEL, decrease the score calculated using the spreadsheet by 

one integer. Situations where there is a NOAEL with no LOAEL can be identified by the lack of a 

critical effect, because the NOAEL was the highest dose tested. 

Step Seven: Choose the higher of the non-cancer or cancer potency scores as the measure of potency. 

Note: if no value for Potency can be found that qualifies for this protocol, please refer the contaminant for 

expert judgment. The only endpoints that may be applied to this protocol are those listed explicitly in the 

hierarchy of values. Further, the only endpoints considered as equivalent to an RfD are MRLs from 

ATSDR, RAISHE RfDs, Cal EPA RfDs, WHO or HC, TDIs, and IOM ULs. 

Exhibit A.1. Potency Scoring Table 

SCORE RfD 

mg/kg-day 

LOAEL/NOAEL 

mg/kg-day 

LD50 

mg/kg 10-4 Cancer Risk 

10 

9 

8 

7 

6 

5 

4 

3 

2 

1 

0 - 0.000000316 

0.000000317 - 0.00000316 

0.00000317 - 0.0000316 

0.0000317 - 0.000316 

0.000317 - 0.00316 

0.00317 - 0.0316 

0.0317 - 0.316 

0.317 - 3.16 

3.17 - 31.6 

31.7 - >31.7 

0 - 0.000316 

0.000317 - 0.00316 

0.00317 - 0.0316 

0.0317 - 0.316 

0.317 - 3.16 

3.17 - 31.6 

31.7 - 316 

317 - 3,160 

3,170 - 31,600 

31,700 - >31,700 

0 - 0.0316 

0.0317 - 0.316 

0.317 - 3.16 

3.17 - 31.6 

31.7 - 316 

317 - 3,160 

3,170 - 31,600 

31,700 - 316,000 

317,000 - 3,160,000 

3,170,000 - >31,700,000 

0 - 0.00000316 

3.17E-06 - 0.0000316 

3.17E-05 - 0.000316 

0.000317 - 0.00316 

0.00317 - 0.0316 

0.0317 - 0.316 

0.317 - 3.16 

3.17 - 31.6 

31.7 - 316 

317 - >317 

A-2 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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A.2 Severity Scoring Protocol 

The score for Severity is based upon the critical effect associated with the data element (RfD, LOAEL, 

etc.) used to score Potency. Potency must be scored prior to Severity. 

Protocol for Severity Scoring 

Step One: Identify the critical effect for the contaminant, based on the data used to score the 

attribute of potency, and enter it into the severity scoring worksheet (shown in Exhibit 

A.2). If the contaminant has more than one critical effect all of the listed effects should 

be included. NOTE: If the critical effect is death and the LD50 data element was used to 

score potency, go to Step Four. If the effects are for a LOAEL from RTECS go to Step 

Five. 

Step Two: Locate the critical effect within the Compendium of Critical Effects Table (see Exhibit 

A.3) and enter the severity score associated with that critical effect in the severity scoring 

worksheet. If a contaminant has more than one critical effect, choose the highest of the 

scores. 

NOTE: If the critical effect is not listed in the Table, go to Step Three. 

Step Three: If the critical effect is not listed in the Table, the scorer should flag that critical effect as 

‘not listed.’ (Health effects experts should be consulted to score these effects.) Once the 

effect is scored it should be added to the compendium for future use and consistent 

scoring. 

Step Four: If a critical effect is not available, or is “death,” use one of the following options for 

scoring: 

1) Search sources identified as supplemental sources for CCL for additional health 

effects data that could be used to score potency and severity for the contaminant. If 

data are found that provide a data element from the potency protocol other than LD50 

to score the contaminant, then that element can be used for scoring. Sources that may 

be most helpful in this search include: Hazardous Substances Data Bank (HSDB), 

International Program on Chemical Safety (INCHEM), and the National Toxicology 

Program (NTP). The element that is found may be used to rescore the contaminant for 

potency, and subsequently severity, using the score associated with the critical effect 

endpoint. 

2) Search for an alternative critical effect associated with the LD50 determination. Locate 

the LD50 study and search for information regarding the types of effects occurring 

prior to animal death. If a critical effect other than death is given in the study, it may 

be used to score the severity of the contaminant. (The potency score is still given by 

the value of the LD50.) 

3) If no additional information can be found, recommend that the contaminant be 

returned to the Universe. 

Step Five If the Potency score is a LOAEL from RTECS, the effects listed represent all effects and 

A-3 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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not just the critical effect (s). There are three available options for improving the scoring 

in this situation. 

1.	 If the RTECS data source is included in the supplemental data, review the 

supplemental information to identify the critical effect. If the supplemental source 

includes a NOAEL for the critical effect, replace the LOAEL with the NOAEL and 

rescore potency if necessary. 

2.	 In cases where the data source for the LOAEL is not in the supplemental data search 

the supplemental data for an alternative data source. If the data identified provides a 

NOAEL or LOAEL that is the same or lower than that in RTECS or is from a study 

of higher quality than the RTECS study , use that NOAEL or LOAEL and its critical 

effect to score both potency and severity. 

3.	 If it is not possible to find better information in the supplemental data sources score 

the most serious of the effects listed in RTECS. 

Exhibit A.2. Severity Scoring Table 

Key Study used to score Potency Critical Effect(s) for Severity Severity Score 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

24 

25 

A-4 
 

 

 

 

 

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Exhibit A.3. Compendium of Critical Effects Table (from Health Advisories & IRIS)
 

For Scoring Severity
 

Severity 

Score 

Score Definition Compendium of Critical Effects 

1 NO ADVERSE EFFECT No observed effect(s). 

No observed adverse effect(s). 

Absence of effects. 

No critical effect(s) identified. 

No effect(s) related to treatment. 

Absence of biologically significant adverse effect(s). 

Absence of gross light microscopic histopathological 

change(s). 

Excedance of the Taste Threshold 

2 COSMETIC EFFECT 

(Interpretation: Consider those effects 

that alter the appearance of the body 

without affecting structure or 

functions) 

Dental fluorosis. 

Abnormal appearance. 

Facial flushing. 

Flushing. 

Argyria. 

Dermal sensitization. 

Skin pigmentation. 

Hyperpigmentation. 

Alopecia. 

Keratosis. 

3 REVERSIBLE EFFECTS; 

DIFFERENCES IN ORGAN 

WEIGHTS OR SIZE, BODY 

WEIGHTS OR CHANGES IN 

BIOCHEMICAL 

PARAMETERS WITH 

MINIMAL CLINICAL 

SIGNIFICANCE. 

(Interpretation: Transient, adaptive 

effects) 

Growth and Weight Effects 

Decreased body weight and or body-weight gain. 

Increased absolute organ weights. 

Increased liver weight. 

Increased kidney weight. 

Increased relative organ weight. 

Decreased relative organ weight. 

Lower ovarian weight. 

Decreased maternal weight gain. 

Increased absolute and relative (to body and/or brain) 

liver weight. 

Increased kidney body weight ratio. 

Increase in spleen weight. 

Increase in thyroid/body weight ratio. 

Changes in thymus weight. 

Decreased body weight. 

Decreased growth. 

Gastrointestinal Disturbances 

Decreased stool quantity. 

Osmotic diarrhea. 

Diarrhea. 

Nausea. 

Vomiting. 

GI irritation. 

GI disturbances. 

A-5 
 

 

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Exhibit A.3. Compendium of Critical Effects Table (from Health Advisories & IRIS)
 

For Scoring Severity
 

Severity 

Score 

Score Definition Compendium of Critical Effects 

3 (cont.) Irritation/Irritability 

Chronic irritation. 

Maternal hyperirritability. 

Chronic irritation without histopathology changes. 

Biochemical Changes 

Decreased glucose. 

Increased blood sugar. 

Increased enzymes. 

Increased triglycerides. 

Increase serum concentration of compound. 

Clinical serum effects. 

Alterations in clinical chemistry. 

Increased serum alkaline phosphatase. 

Significant elevation of serum calcium levels. 

Enzyme inhibition, induction, or change in blood 

tissue levels 

Decreased ESOD activity. 

Decrease in erythrocyte superoxide dismutase 

(ESOD) concentration. 

Minor alteration in clinical chemistry, e.g., decrease 

in erythrocyte superoxide dismutase (ESOD). 

Hematological effects 

Hematological effects. 

Abnormal pigments in blood. 

Decreased lymphocyte count. 

Decreased blood counts. 

Decreased white blood cells. 

Methemoglobinemia. 

Increased carboxyhemoglobin. 

Hemosiderosis. 

Anemia. 

Normocytic anemia. 

Iron deposits and elevated Heinz bodies in liver. 

Decreased hemoglobin and possible erythrocyte 

destruction. 

Decreased RBC, packed cell volume, and 

hemoglobin. 

Hematologic, hepatic, and renal toxicity as evidenced 

by a statistically significant decrease in 

hemoglobin, hematocrit, and RBC levels. 

RBC and liver effects as evidenced by increase Heinz 

bodies in RBC. 

Sporadic decrease in hemoglobin and RBC. 

Decreased RBC and hematocrit. 

A-6 
 

 

 

 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Exhibit A.3. Compendium of Critical Effects Table (from Health Advisories & IRIS)
 

For Scoring Severity
 

Severity Score Definition Compendium of Critical Effects 

Score 

3 (cont.) Cholinesterase Effects 

Reversible PChE (plasma) or RBC-ChE inhibition 

without cholinergic symptoms or signs 

RBC ChE depression without cholinergic symptoms 

or sweating. 

Plasma cholinesterase (ChE) inhibition without 

cholinergic symptoms or sweating. 

Hormone Changes 

Decrease in T3, T4. 

Dose-related decrease in T4, T3, and increase TSH. 

Elevated thyroid stimulating hormone (TSH) 

concentration. 

ACTH decrease. 

Cellular Vacuolization 

Mild to moderate vacuolization 

Tubular epithelial vacuolization. 

Brain cell vacuolization. 

Additional Effects 

Changes in teeth and supporting structures. 

Sensory organ effects. 

Centrilobular eosinophilic liver changes. 

Possible vascular complication. 

Inhibition of the concentration of beneficial bacteria 

in the gastrointestinal microflora 

4 CELLULAR/PHYSIOLOGICAL Hematological Effects 

CHANGES THAT COULD Jaundice. 

LEAD TO DISORDERS (risk Anemia 

factors or precursor effects). Hemolytic anemia. 

(Interpretation: Considers Erythrocyte destruction. cellular/physiological changes in the Hemolysis. 

body that are used as indicators of 

disease susceptibility) Immunological Effects 

Decreased delayed hypersensitivity response. 

Decrease in cellular immune response. 

Decrease in humoral immune response. 

Liver Effects 

Fatty cyst - liver and elevated liver enzymes (i.e., 

SGPT, LDH). 

Liver cell enlargement or alteration. 

Liver cell polymorphism. 

Proteinuria. 

Renal cytomegaly. 

Cholinergic Effects 

Cholinesterase inhibition with symptoms. 

Cholinergic signs or symptoms. 

Other Effects 

A-7 
 

 

 

 

 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Exhibit A.3. Compendium of Critical Effects Table (from Health Advisories & IRIS)
 

For Scoring Severity
 

Severity 

Score 

Score Definition Compendium of Critical Effects 

Hypothermia 

Mild CNS Effects 

5 SIGNIFICANT FUNCTIONAL 

CHANGES THAT ARE 

REVERSIBLE OR 

PERMANENT CHANGES OF 

MINIMAL TOXICOLOGICAL 

SIGNIFICNACE. 

(Interpretation: Consider those 

disorders in which the removal of 

chemical exposure will restore health 

back to prior condition) 

Increased cholinergic effects 

ChE inhibition with sweating, diarrhea, hypotention, 

and/or fishy body odor.. 

RBC and/or plasma acetylcholinesterase (AChE) 

inhibition with cholinergic symptoms or sweating. 

Brain acetylcholineesterase inhibition with or without 

signs or symptoms 

Hematological Effects 

GI bleeding. 

Coagulation defects. 

Extramedulary hematopoesis 

Tendency to hemorrhage. 

Structural Effects 

Rachitic bone. 

Renal Effects 

Renal cytomegaly. 

Renal effects/toxicity (increased uric acid levels; 

increased urinary coproporphyrins). 

Inflammatory foci – kidneys. 

Hepatic Effects 

Liver function tests impaired. 

Fatty-cyst in liver hemosiderosis. 

Multiple Organ Effects 

Effects on the lungs, liver, kidney, thyroid and 

thyroid hormones. 

Ocular Effects 

Corneal damage. 

Neurological Effects 

Mild neurological signs. 

Alteration of classic conditioning. 

Brain ChE inhibition. 

Myelin degeneration. 

CNS depression. 

Brain/ other coverings- recordings from specific 

areas of CNS. 

Tremors. 

Dyspnea. 

Changes in motor activity. 

Hypoactivity. 

Ataxia. 

A-8 
 

 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Exhibit A.3. Compendium of Critical Effects Table (from Health Advisories & IRIS)
 

For Scoring Severity
 

Severity 

Score 

Score Definition Compendium of Critical Effects 

5 (cont.) Other Effects 

Chronic pneumonitis. 

Clinical selenosis. 

Nonneoplastic lesions - splenic capsule. 

Intestinal lesions. 

Splenomegaly 

6 SIGNIFICANT, 

IRREVERSIBLE, 

NONLETHAL CONDITIONS 

OR DISORDERS. 

(Interpretation: Consider those 

disorders that persist for over a long 

period of time but do not lead to death) 

Multiple Organ Effects 

Histopathological effects in liver, kidney, and 

thyroid. 

Minimal to moderate congestion of liver, kidney, and 

lungs. 

Liver and kidney pathology. 

Kidney and spleen pathology. 

Hepatic Effects 

Hepatic lesions/necrosis. 

Hepatocyte degeneration. 

Hepatotoxicity. 

Liver cell polymorphism. 

Liver effects/toxicity. 

Liver lesions. 

Renal Effects 

Atrophy and degeneration of the renal tubules – 

nephropathy (unspecified). 

Kidney toxicity. 

Mineralization of the kidneys. 

Renal dysfunction. 

Renal effects/toxicity (increased uric acid levels; 

increased urinary coproporphyrins). 

Functional and histopathological effects in kidney. 

Kidney damage (unspecified). 

Kidney lesions (unspecified). 

Impaired renal clearance/function. 

Tubular epithelial vacuolation. 

Sensory and Neurological Effects 

Significant decrease in brain and brain to body 

weight ratio. 

Degenerative changes for brain/ other coverings. 

Peripheral neuropathy- neuropathy (unspecified). 

Neurotoxicity. 

Nerve damage (unspecified). 

Optic nerve degeneration/ damage. 

Sensory neuropathy. 

Minimal lens opacity and cataracts. 

Nasal olfactory lesions. 

A-9 
 

 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Exhibit A.3. Compendium of Critical Effects Table (from Health Advisories & IRIS)
 

For Scoring Severity
 

Severity 

Score 

Score Definition Compendium of Critical Effects 

6 (cont.) Hyperplasia 

Thyroid hyperplasia. 

Urothelial hyperplasia. 

Hyperplasia. 

Squamous and basal hyperplasia of the 

forestomach. 

Epithelial hyperplasia – forestomach. 

Cardiac Effects 

Cardiac toxicity. 

Cardiomyopathy, including infarction. 

Vascular complications. 

Right atrial dilation. 

Convulsions. 

Mild histological lesions. 

Other Effects 

Gastrointestinal necrotic changes. 

Chronic irritation with histopathology findings. 

Forestomach lesions (unspecified). 

Organ atrophy. 

Thyroid effects (unspecified). 

Thyroid mineralization. 

Spleen toxicity (unspecified). 

Bladder toxicity (unspecified). 

Bone marrow toxicity (unspecified). 

Hormonal response to extrogenic substances in post 

menopausal women. 

7 DEVELOPMENTAL OR 

REPRODUCTIVE EFFECTS 

LEADING TO MAJOR 

DYSFUNCTION. 

(Interpretation: Considers those 

chemicals that cause permanent 

developmental effects or that impact 

the ability of a population to 

reproduce) 

Reproductive Organ Effects 

Testicular atrophy/damage. 

Testicular and uterine effects. 

Atrophied seminiferous epithelium. 

Histopathological changes in testes. 

Hypospadia. 

Lesions observed in reproductive organs. 

Decreased testes weight and testes to body weight 

ratio, atrophied seminiferous epithelium; and 

decreased tubular size in testes. 

Endometriosis. 

Decreased tubular size in testes. 

Decreased ovarian weight and function. 

Altered cellular foci. 

Maternal Toxicity 

Maternal toxicity. 

Decreased maternal weight gain. 

A-10 
 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Exhibit A.3. Compendium of Critical Effects Table (from Health Advisories & IRIS)
 

For Scoring Severity
 

Severity 

Score 

Score Definition Compendium of Critical Effects 

7 (cont.) 

Fertility effects 

Spermatogenic arrest. 

Reduced numbers of corpora allata. 

Reduced or deformed sperms. 

Adverse reproductive effects. 

Reduction in fertility. 

Decreased fertility index. 

Decrease in size of litter. 

Growth inhibition 

Reduced offspring weight gain, total litter weight, or 

litter size. 

Decreased pup weight 

Decreased lactation indices. 

Increased runt incidence. 

Decreased crown-rump length 

Decreased offspring viability 

Excessive loss of litters 

Increase in number of stillbirths. 

Maternal and fetal toxicity. 

Increased intrauterine death. 

Decreased pup survival or viability. 

Increased abortion rate. 

Increase in number of stillbirths. 

Increased dead pups at birth. 

Decreased pup viability index. 

Parturition mortality. 

Fetal resorptions. 

Developmental effects 

Fetal toxicity/malformations. 

Developmental toxicity (skeletal or visceral 

abnormalities). 

Delayed ossification. 

Neurodevelopmental effects. 

Brain cell vacuolization in neonates. 

Myelin degeneration. 

Skeletal or visceral abnormalities (Extra ribs and 

other measures of sexual maturation). 

Increased retinal folds in weanlings. 

Mixed sexual differentiation (i.e., effeminization or 

emasculanization). 

Imbalance in sex ratio. 

8 TUMORS OR DISORDERS 

LIKELY LEADING TO DEATH 

(Interpretation: Considers chemical 

exposures that result in a fatal disorder 

and all types of tumors). 

Cancer. 

Suspected carcinogenicity (including short latency 

periods and rare tumors). 

Any type of cancer. 

A-11 
 

 

 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Exhibit A.3. Compendium of Critical Effects Table (from Health Advisories & IRIS)
 

For Scoring Severity
 

Severity 

Score 

Score Definition Compendium of Critical Effects 

9 DEATH. Increased mortality. 

Longevity. 

Mortality. 

Survival. 

Decreased survival. 

Increased mortality. 

Decreased adult survival. 

Decreased adult longevity. 

High incidence of mortality at early age (i.e., 25% to 

50% by mid-life) in chronic studies. 

Maternal death during pregnancy. 

Reduced longevity. 

Death. 

A.3 Prevalence Scoring Protocol 

This section describes how to assign a numerical score for the attribute Prevalence. 

Step One: Identify highest-ranked data value 

When more than one data value is available for a particular contaminant candidate, use the hierarchy in 

Exhibit A.4. Use the same type of data to score Prevalence as for Magnitude. 

Exhibit A.4. Hierarchy of Prevalence Data Elements 

Rank Prevalence Data Element Type of Data 

1 Finished Drinking Water – Percentage of all 

Public Water Systems (PWSs) with Detections 

(If data from both NCOD Round 1 and Round 2 

are available, use the higher of the values.) 

National scale / representative data (data 

from UCMR has highest priority, then 

NCOD, then NIRS) 

2a Percentage of all Ambient/Raw/Source 

Monitoring Samples or Sites with Detections 

National scale / representative data 

(NAWQA) 

2b Percentage of Ambient/Raw/Source Monitoring 

Samples or Sites with Detections (Note: use 

combined surface / ground water if available 

and higher of SW/GW if not) 

National scale / representative data (NREC 

– first use National Reconnaissance data, 

then National Aggregate data) 

3 Pesticide application data, number of states 

where pesticide was applied 

From NCFAP 

4 Environmental release data, number of states 

reporting releases 

From TRI 

5 Production volume data From Chemical Update System/ Inventory 

Update Rule (CUS/IUR) 

A-12 
 

 

 

 

 

 

 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Step Two: Use scoring table to find attribute score for value identified in Step One. 

For each element there is a corresponding column in the Prevalence Scoring table (see Exhibit A.5), 

which contains a range of data values assigned to a numeric prevalence score between 1 and 10. Once a 

data value has been found for a particular element, look up the value in Exhibit A.5 to determine the 

prevalence score. For CUS/IUR data, use the most recent year reported. For pesticides, if the compound is 

a degradate and does not have its own data, use the parent to score. 

Exhibit A.5. Prevalence Scoring Scales 

Hierarchy 

Prevalence 

Score 

1 2 3 4 5 

% Finished 

Water PWSs 

with detections 

of contaminant 

All PWSs 

% Ambient 

water sites 

with 

detections of 

contaminant 

All 

sites/samples 

# States Reporting 

Pesticide in Use # of States 

Reporting TRI 

total releases 

CUS/IUR 

(production data) 

Number of pounds 

(by category) 

produced 

1 <=0.10 <=0.10 -- 1 <500K 

2 0.11-0.16 0.11-0.16 -- 2 --

3 0.17-0.25 0.17-0.25 

Default for any 

pesticide in nonenvironmental use 3 >500K-1M 

4 0.26-0.44 0.26-0.44 -- 4 --

5 0.45-0.61 0.45-0.61 

Default for any 

pesticide in 

environmental use 

without data 5 >1M-10M 

6 0.62-1.00 0.62-1.00 <6 6 >10M-50M 

7 1.01-1.30 1.01-1.30 6-10 7-10 >50M-100M 

8 1.31-2.50 1.31-2.50 11-15 11-15 >100M-500M 

9 2.51-10.00 2.51-10.00 16-25 16-25 >500M-1B 

10 >10.00 >10.00 >25 >25 >1B 

Note:
 

Use data in the highest category to score.
 

For CUS/IUR data, use the most recent year reported. Not Reported means there has been no change
 

in production volume since the last report.
 

For pesticides, if the compound is a degradate and does not have its own data, use the parent to
 

score.
 

A-13 
 

 

 

 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

A.4 Magnitude Scoring Protocol 

This section describes how to assign a numerical score for the attribute Magnitude. 

Step One: Identify the highest-ranked data element 

When more than one data element is available for a particular contaminant, use the hierarchy below to 

select the preferred element. Exhibit A.6 presents the hierarchy of data elements to be used in the 

Magnitude scoring process. Note that the Magnitude element should be correlated with the value used to 

score the attribute Prevalence, except when production data are used for Prevalence and PersistenceMobility is used for Magnitude. 

Exhibit A.6. Hierarchy of Magnitude Data Elements 

Rank Magnitude Data Element Type of Data 

1 Finished Drinking Water – Median of 

detected concentrations from all Public 

Water Systems with detections (If data 

from both NCOD Round 1 and Round 2 

are available, use the higher of the 

values.) 

National scale finished drinking water occurrence 

data [data from Unregulated Contaminant 

Monitoring Rule (UCMR) has highest priority, then 

the National Contaminant Occurrence Database 

(NCOD), then the National Inorganics 

Reconnaissance Survey (NIRS)] 

2a Median of detected concentrations from 

all ambient / raw source monitoring sites 

with detections 

National scale ambient monitoring data (National 

Water Quality Assessment Program - NAWQA) 

2b Median of detected concentrations from 

ambient / raw / source water samples 

with detections (Note: use combined 

surface / ground water if available and 

higher of SW/GW if not) 

National scale / representative data (National 

Reconnaissance of Emerging Contaminants - NREC 

– first use National Reconnaissance data, then 

National Aggregate data) 

3 Pesticide application data From National Center for Food and Agricultural 

policy (NCFAP) 

4 Environmental release data, total 

pounds or tons reported as released 

(TRI) 

From Toxics Release Inventory (TRI) 

5 Persistence – Mobility (Environmental 

Fate Data) 

Physical chemical properties 

Step Two: Use scoring table to find attribute score for value identified in Step One. 

For each data element, there is a corresponding column in the Magnitude Scoring table (Exhibit A.7), 

which contains a range of data values assigned to a numerical magnitude score. Locate the column in the 

table associated with the highest-ranking data element identified in step one. Use the information in the 

column to determine the numerical score associated with the data value for the chemical being scored. 

The number corresponding to each "Score" is the maximum in that category, e.g. 0.1 μg/L for finished 

A-14 
 

 

 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

water scores 4, not 5. In cases where there are no data for Scoring Magnitude in Exhibit A.7 (e.g. 

Prevalence is scored using Production Volume data), use the Persistence-Mobility Scoring approach to 

develop a Magnitude Score. 

Persistence-Mobility Scoring 

The approach for scoring persistence and mobility includes assigning two values, one for persistence and 

one for mobility, on a numeric scale of 1 through 3, representing low, medium, and high for each property 

as it favors the presence of the contaminant in water. Using a hierarchy of physical property data 

elements, each contaminant is scored for both persistence and mobility. The average of these two values 

is multiplied by 10/3 to obtain the persistence-mobility score. Exhibit A.8 displays the hierarchy of 

available properties for each data element representing either persistence or mobility. 

Protocol for Persistence-Mobility Scoring 

Step One: Identify and score highest-ranked data value for Persistence 

When more than one data element value is available for a particular contaminant candidate, use the 

hierarchy below to select the preferred element. Exhibit A.6 describes the hierarchy of data elements to be 

used in the Persistence scoring process. When several values for a physical property are available, the 

highest scoring value should be used, unless that value is not representative of environmental conditions 

in drinking water. 

Step Two: Identify and score highest-ranked data value for Mobility 

The hierarchy of physical properties for scoring mobility is given in Exhibit A.6. Select the highest 

priority data element available for scoring. When several values for a particular physical property are 

available, the highest scoring value should be used for scoring, unless that value is not representative of 

environmental conditions in drinking water. 

Step Three: Multiply the average of the persistence and mobility values by 10/3 for the 

magnitude score. 

A-15 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Exhibit A.7. Magnitude Scoring Scales 

Hierarchy 

Magnitude 

Scale 

1 2 3 4 5 

Finished Water 

Occurrence 

Scale 

Ambient Water 

Occurrence 

Scale 

Pesticide Use 

Scale 

TRI Total 

Releases Scale 

Persistence/ 

Mobility 

Data Used 

to Score 

Median of 

detections - all 

PWSs 

Median of 

detections - all 

sites/samples 

Number of pounds 

applied 

Total number of 

pounds released 

Units μg/L μg/L lbs lbs Used when 

Production 

data are used 

to score for 

prevalence. 

See 

Persistence/ 

Mobility 

protocol 

(Exhibit A.8) 

Score 

1 <0.003 <0.003 <10,000 <300 

2 0.003 - 0.01 0.003 - 0.01 - - 301-1,000 

3 >0.01 - 0.03 >0.01 - 0.03 10,000-30,000 1,001-3,000 

4 >0.03 - 0.1 >0.03 - 0.1 30,001-100,000 3,001-10,000 

5 >0.1 - 0.3 >0.1 - 0.3 100,001-300,000 10,001-30,000 

6 >0.3 - 1 >0.3 – 1 300,001-1M 30,001-100,000 

7 >1 - 3 >1 – 3 1M - 3M 100,001-300,000 

8 >3 - 10 >3 – 10 3M - 10M 300,001-1M 

9 >10 - 30 >10 – 30 10M - 30M 1M - 3M 

10 >30 >30 >30M >3M 

Notes:
 

Use data in the highest category to score.
 

The number corresponding to each "Score" is the maximum in that category, e.g. 0.1 μg/L scores 4, not 5.
 

For pesticides, use the parent to score if the compound is a degradate and does not have its own data.
 

Exhibit A.8. Magnitude Scales for Environmental Fate Data
 

Magnitude Hierarchy 5
 

Mobility Scale Value
 

Units 1 (Low) 2 (Medium) 3 (High) 

Organic Carbon 

Partitioning Coefficient 

(Koc) mL/g >1,000 100-1,000 <100 

Log Octanol/Water 

Partitioning Coefficient 

(log Kow) dimensionless >4 1-4 <1 

Soil/Water Distribution 

Coefficient (Kd) mL/g >10 1-10 <1 

Henry's Law Coefficient 

(KH) atm-m3

/mol >10-3 10-7-10-3 <10-7 

Henry's Law Coefficient 

(KH) dimensionless >0.042 

0.042-

4.2x10-6 <4.2x10-6 

A-16 
 

 

 

 

 

 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Exhibit A.8. Magnitude Scales for Environmental Fate Data 

Magnitude Hierarchy 5 

Mobility Scale Value 

Solubility 

Percent in water (PBT 

Profiler) 

Units 

mg/L 

dimensionless 

1 (Low) 

<1 

≤ 25 

2 (Medium) 

1-1,000 

>25-50 

3 (High) 

>1,000 

> 50 

Persistence Scale Value 

Units 1 (Low) 2 (Medium) 3 (High) 

Half Life (t1/2) time 

days, 

daysweeks 

weeks, 

weeksmonths 

months, 

recalcitrant 

Measured Degradation 

Rate1 time 

days, 

daysweeks 

(BF, 

BFA)2 

weeks, 

weeksmonths 

(BS, BSA) 

months, 

recalcitrant 

(BST) 

Modeled Degradation 

Rate (PBT Profiler) time 

days, 

daysweeks 

weeks, 

weeksmonths months, 

recalcitrant 1 When two results are found for a measured degradation rate, the data are "averaged" and then a 

value determined.
 2 BF = Biodegrades Fast, BFA = Biodegrades Fast with Acclimation, BS = Biodegrades Slow, BST =
 

Biodegrades Sometimes.
 

A-17 
 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-08-008 

Classification of the PCCL to CCL August 2009 

Contaminant 3 

Appendix B. Example Blinded Information Sheets from the TDS Exercises 

Contaminant Name: 

It is a volatile organic chemical. It is used as a wetting and dispersing agent in textile processing, dye-baths, stain and printing compositions; used in cleaning and 

detergent preparations, adhesives, cosmetics, deodorants, fumigants, emulsions and polishing compositions. Used in lacquers, paints, varnishes, paint and varnish 

removers. Degreasing agent. It is on the TSCA list. The reportable released quanitity of this substance under CERCLA is 1 lb. It is also subject to RCRA waste 

management requirements, and is listed as a hazardous air pollutant by EPA. Several states have drinking water guidelines for this chemical (CA, FL, MA, ME, NC). 

Its one-day Health Advisory Level (HAL) is 4,000 μg/L, its 10-day HAL is 400 μg/L, and its 10^-4 cancer risk HAL is 300 μg/L. This is an HPV chemical. It is also on the 

Background: 

CCL. (HSDB, 2005; EPAHA, 2004) 

HEALTH EFFECTS DATA 

Data Element Value Units Source Notes 

Reference Dose N/A 

Carcinogen classification (EPA) B2 (probable human carcinogen) IRIS 9/1/1990 

Slope Factor 0.011 1/(mg/kg-d) IRIS 9/1/1990 

Carcinogen Classification (IARC) 2B (possible) IARC 

Non EPA Derived Dose1 0.1 mg/kg-d ATSDR MRL Chronic oral 

Critical Effect Hepatic effects UF=100 

File/Issue Date 10/1/2004 

Lowest Oral Chronic LOAEL1 N/A 

Lowest Oral LD501 N/A 

Is contaminant on list of carcinogens? Y Y/N 

Cal EPA Chemicals Known to the 

State to Cause Cancer or 

Reproductive Toxicity 

1/1/1988 

Is the contaminant on a list of reproductive 

toxins? N Y/N 

Cal EPA Chemicals Known to the 

State to Cause Cancer or 

Reproductive Toxicity 

Risk assessment ongoing? Y Y/N 

Health Reference Level (HRL)2 700 μg/L Based on MRL 

Health Reference Level (HRL)2 cancer 3.18 μg/L 

Health Reference Level (HRL) cancer 300 μg/L 10-4 cancer risk Health Advisory (EPAHA, 1987) 

Notes 

1

 Non-EPA toxicology data will be sought if no EPA Reference Dose or carcinogen information available; may require multiple entries; chronic studies will be prioritized over short term studies. 

2

 Health Reference Level calculated by conversion of RfD or other dose to units of μg/L, assuming 2 liters per day of water consumed by a 70 Kg adult, and a default Relative Source 

Contribution of 20%. For carcinogens, the concentration at the 10-6 cancer risk level will be converted to units of μg/L and will also be listed. 

B-1 
 

 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-08-008 

Classification of the PCCL to CCL August 2009 

Contaminant 3 

OCCURRENCE DATA 

Water Occurrence Data # PWSs/Sites 

sampled # with Detects % Detects 

Minimum 

of Detects 

(μg/L) 

Maximum 

of Detects 

(μg/L) 

Median of 

Detects 

(μg/L) 

99% of 

Detects 

(μg/L) 

Source Notes 

Finished Water Occurrence - total No Data No Data No Data No Data No Data No Data No Data 

# PWSs/Sites 

sampled # with Detects % Detects 

Minimum 

of Detects 

(μg/L) 

Median of 

Detects 

(μg/L) 

Mean of 

Detects 

(μg/L) 

90% of 

Detects 

(μg/L) 

95% of 

Detects 

(μg/L) 

99% of 

Detects 

(μg/L) 

Maximum 

of Detects 

(μg/L) 

Source 

Source Water-Total No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data 

Production/Release Value Units Source Notes 

Production data >1M - 10M lbs/yr CUS-IUR (2002) 

Pesticide Application - total N/A lbs/yr 

Pesticide Application - total (# States) N/A # States 

Release - total 1,146,641 lbs/yr TRI 

Release - total (# States) 22 # States TRI 

Release - to Surface Water 75,119 lbs/yr TRI 

Release - to SW (# States) 9 # States TRI 

Environmental Fate Parameters Value Units Source Notes 

T1/2, Half life No Data length of time 

KOC, Organic Carbon Partition Coefficient 1 L/kg RAISCF 

KOW, Octanol Water Partition Coefficient Log -0.27 unitless RAISCF 

HLC, Henry's Law Constant 0.000196 unitless RAISCF 

Water Solubility 1,000,000 mg/L RAISCF 

Kd, Distribution Coefficient N/A source specific 

No Data = No data found for this contaminant; N/A = Not applicable to contaminant 

B-2 
 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-08-008 

Classification of the PCCL to CCL August 2009 

Contaminant 4 

Contaminant Name: 

Background: This is a volatile organic chemical. It is used as a food additive, organic intermediate, solvent, and in cosmetic formulations. It is also used as a solvent or 

solubilizer in the paint and printing ink sector, as components in textile auxiliaries and pesticides, for hormone extraction, and in the surfactant field as foam 

boosters or antifrothing agents. Per the FDA, this food additive is permitted for direct addition to food for human consumption as a synthetic flavoring substance 

and adjuvant. (HSDB, 2005) 

HEALTH EFFECTS DATA 

Data Element Value Units Source Notes 

Reference Dose N/A 

Carcinogen classification (EPA) N/A 

Slope Factor N/A 

Carcinogen Classification (IARC) N/A 

Non EPA Derived Dose1 N/A 

Lowest Oral Chronic LOAEL1 N/A 

Lowest Oral LD501 500 mg/kg RTECS Critical effect: Ataxia, irritability, dyspnea, acute pulmonary edema 

Is contaminant on list of carcinogens? N Y/N 

Cal EPA Chemicals Known to the 

State to Cause Cancer or 

Reproductive Toxicity 

Is the contaminant on a list of 

reproductive toxins? N Y/N 

Cal EPA Chemicals Known to the 

State to Cause Cancer or 

Reproductive Toxicity 

Risk assessment ongoing? N Y/N 

Health Reference Level (HRL)2 N/A μg/L 

Health Reference Level (HRL)2 cancer N/A μg/L 

Notes
 1

 Non-EPA toxicology data will be sought if no EPA Reference Dose or carcinogen information available; may require multiple entries; chronic studies will be prioritized over short term studies.
 2

 Health Reference Level calculated by conversion of RfD or other dose to units of μg/L, assuming 2 liters per day of water consumed by a 70 Kg adult, and a default Relative Source
 

Contribution of 20%. For carcinogens, the concentration at the 10-6 cancer risk level will be converted to units of μg/L and will also be listed.
 

B-3 
 

 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-08-008 

Classification of the PCCL to CCL August 2009 

Contaminant 4 

OCCURRENCE DATA 

Water Occurrence Data # PWSs/Sites 

sampled # with Detects % Detects 

Minimum 

of Detects 

(μg/L) 

Maximum 

of Detects 

(μg/L) 

Median of 

Detects 

(μg/L) 

99% of 

Detects 

(μg/L) 

Source Notes 

Finished Water Occurrence - total No Data No Data No Data No Data No Data No Data No Data 

# PWSs/Sites 

sampled # with Detects % Detects 

Minimum 

of Detects 

(μg/L) 

Median of 

Detects 

(μg/L) 

Mean of 

Detects 

(μg/L) 

90% of 

Detects 

(μg/L) 

95% of 

Detects 

(μg/L) 

99% of 

Detects 

(μg/L) 

Maximum 

of Detects 

(μg/L) 

Source 

Source Water-Total No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data 

Production/Release Value Units Source Notes 

Production data >500K - 1M lbs/yr CUS-IUR (2002) 

Pesticide Application - total N/A lbs/yr 

Pesticide Application - total (# States) N/A # States 

Release - total No Data lbs/yr 

Release - total (# States) No Data # States 

Release - to Surface Water No Data lbs/yr 

Release - to SW (# States) No Data # States 

Environmental Fate Parameters Value Units Source Notes 

T1/2, Half life No Data length of time 

KOC, Organic Carbon Partition Coefficient 15 L/kg HSDB 

KOW, Octanol Water Partition Coefficient Log 2.62 unitless HSDB 

HLC, Henry's Law Constant 1.88E-05 atm-cu m/mol HSDB 

Water Solubility 1000 mg/L HSDB 

Kd, Distribution Coefficient N/A source specific 

No Data = No data found for this contaminant; N/A = Not applicable to contaminant 

B-4 
 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-08-008 

Classification of the PCCL to CCL August 2009 

Contaminant 5 

Contaminant Name: 

Background: This is a volatile organic chemical registered for use in the U.S. Nematicide. Seventh most commonly used pesticide in U.S. agricultural crop production. Used 

in organic synthesis and in manufacture of pesticides. Pre-plant soil fumigant. It is listed on FIFRA and TSCA. The reportable release quantity under CERCLA 

is 100 lbs. It is subject to RCRA waste management requirements. It is listed as a hazardous air pollutant and as a hazardous substance by the Federal Water 

Pollution Control Act and the Clean Water Act. It has a state drinking water standard in CA. It has a state drinking water guideline in several states (FL, MA, 

ME, MN, WI). It has a DWEL of 1,000 μg/L, and its one-day and ten-day Health Advisory Levels (HALs) are 30 μg/L. This is an HPV chemical. (HSDB, 2005; 

EPAHA, 2004) 

HEALTH EFFECTS DATA 

Data Element Value Units Source Notes 

Reference Dose 0.03 mg/kg-d IRIS Basis = BMDL(10) 3.4 mg/kg-d Rat, UF=100, MF=1 

Critical Effect Chronic irritation Confidence: Study: High; Database: High; RfD: High 

File/Issue Date 5/25/2000 

Reference Dose 0.025 mg/kg-d OPP Basis = NOEL 2.5 mg/kg-d Rat, UF=100, MF=1 

Critical Effect decrease in body weight gain and an increase in the incidence of basal 

cell hyperplasia of the nonglandular mucosa of the stomach 

File/Issue Date 1998 

Carcinogen classification (EPA) B2; inadequate in humans, sufficient in animals IRIS 5/25/2000 

Slope Factor 0.1 1/(mg/kg-d) IRIS 

Carcinogen Classification (IARC) 2B (possible) IARC 

Non EPA Derived Dose1 N/A 

Lowest Oral Chronic LOAEL1 N/A 

Lowest Oral LD501 N/A 

Cal EPA Chemicals Known to the 

Is contaminant on list of carcinogens? Y Y/N State to Cause Cancer or 1/1/1989 

Reproductive Toxicity 

Is the contaminant on a list of 

reproductive toxins? N Y/N 

Cal EPA Chemicals Known to the 

State to Cause Cancer or 

Reproductive Toxicity 

Risk assessment ongoing? N Y/N 

Health Reference Level (HRL)2 210 μg/L Based on IRIS RfD 

Health Reference Level (HRL)2 cancer 0.35 μg/L Based on IRIS slope factor 

Health Reference Level (HRL) cancer 40 μg/L 10-4 cancer risk Health Advisory (EPAHA, 1988) 

Notes
 1

 Non-EPA toxicology data will be sought if no EPA Reference Dose or carcinogen information available; may require multiple entries; chronic studies will be prioritized over short term studies.
 2

 Health Reference Level calculated by conversion of RfD or other dose to units of μg/L, assuming 2 liters per day of water consumed by a 70 Kg adult, and a default Relative Source
 

Contribution of 20%. For carcinogens, the concentration at the 10-6 cancer risk level will be converted to units of μg/L and will also be listed.
 

B-5 
 

 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-08-008 

Classification of the PCCL to CCL August 2009 

Contaminant 5 

OCCURRENCE DATA 

Water Occurrence Data # PWSs/Sites 

sampled # with Detects % Detects 

Minimum 

of Detects 

(μg/L) 

Maximum 

of Detects 

(μg/L) 

Median of 

Detects 

(μg/L) 

99% of 

Detects 

(μg/L) 

Source Notes 

Finished Water Occurrence - total 9,164 15 0.16% 0.5 2 1 2 NCOD 

Round 1 

Finished Water Occurrence - SW 898 5 0.56% 1 2 1.25 2 NCOD 

Round 1 

Finished Water Occurrence - GW 8,303 10 0.12% 0.5 1.6 0.5 1.6 NCOD 

Round 1 

Finished Water Occurrence - total 

Finished Water Occurrence - SW 

Finished Water Occurrence - GW 

16,787 

1,609 

15,178 

58 0.35% 0.2 39 

10 0.62% 0.2 1.6 

48 0.32% 0.2 39 

0.5 

0.5 

0.5 

39 NCOD 

Round 2 

1.6 NCOD 

Round 2 

39 NCOD 

Round 2 

Source Water-Total 

# PWSs/Sites 

sampled 

No Data 

# with Detects % 

Detects 

Minimum 

of Detects 

(μg/L) 

Median of 

Detects 

(μg/L) 

No Data No Data No Data No Data 

Mean of 

Detects 

(μg/L) 

No Data 

90% of 

Detects 

(μg/L) 

95% of 

Detects 

(μg/L) 

No Data No Data 

99% of 

Detects 

(μg/L) 

Maximum 

of Detects 

(μg/L) 

No Data No Data 

Source 

Production/Release Value Units Source Notes 

Production data >1M - 10M lbs/yr CUS-IUR (2002) 

Pesticide Application - total 34,717,237 lbs/yr NCFAP 

Pesticide Application - total (# States) 20 # States NCFAP 

Release - total 10,532 lbs/yr TRI 

Release - total (# States) 8 # States TRI 

Release - to Surface Water 85 lbs/yr TRI 

Release - to SW (# States) 3 # States TRI 

Environmental Fate Parameters Value Units Source Notes 

T1/2, Half life No Data length of time 

KOC, Organic Carbon Partition Coefficient 81 L/kg RAISCF 

KOW, Octanol Water Partition Coefficient Log 2.03 unitless RAISCF 

HLC, Henry's Law Constant 0.145 unitless RAISCF 

Water Solubility 2,800 mg/L RAISCF 

Kd, Distribution Coefficient N/A source specific 

No Data = No data found for this contaminant; N/A = Not applicable to contaminant 

B-6 
 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to the CCL August 2009 

Appendix C. CCL 3 Training Data Set (TDS) and Summary of EPA TDS Decisions. For detailed Chemical 

Information Sheets for the TDS go to www.regulations.gov Docket ID EPA-HQ-OW-2007-1189. 

Chemical ID INPUT ATTRIBUTE SCORES 

Team Consensus Blinded 

Decisions 

Chemical 

Algorithm 

Number CASRN Contaminant Name Potency Severity Prevalence Magnitude 

List=4 

Mean 

Integer 

Score L/NL 

5 75343 1,1-Dichloroethane 6 8 6 7 3.50 4 L 

16 563586 1,1-Dichloropropene 4 5 2 6 1.67 2 NL? 

91 87843 1,2,3,4,5-Pentabromo-6- 5 8 1 5 2.20 2 NL? 

22 95636 1,2,4-Trimethylbenzene 4 3 6 6 1.50 2 NL? 

53 122667 1,2-Diphenylhydrazine 6 8 1 1 1.17 1 NL 

10 142289 1,3-Dichloropropane 5 6 1 6 1.67 2 NL? 

48 123319 1,4-Benzenediol 4 3 8 8 2.33 2 NL? 

49 123911 1,4-Dioxane 4 8 9 9 3.67 4 L 

101 111706 1-Heptanol (4) 5 6 3 7 2.60 3 L? 

51 542756 1-Propene, 1,3-dichloro- 5 8 4 6 2.83 3 L? 

20 594207 2,2-Dichloropropane 4 5 2 6 1.50 2 NL? 

37 88062 2,4,6-Trichlorophenol 7 3 1 1 1.00 1 NL 

52 99558 2-Methyl-5-nitroaniline 5 8 1 1 1.17 1 NL 

4 109068 2-Methylpyridine (2) 4 3 7 6 1.83 2 NL? 

54 75070 Acetaldehyde 5 5 10 10 3.50 4 L 

24 34256821 Acetochlor 5 7 1 1 1.17 1 NL 

55 107028 Acrolein 6 9 3 7 3.00 3 L? 

26 142363539 Alachlor ESA 3 3 9 3 1.50 2 NL? 

33 309002 Aldrin 8 8 1 6 2.83 3 L? 

21 7429905 Aluminum 3 6 9 10 3.17 3 L? 

19 62533 Aniline 5 6 9 8 3.17 3 L? 

11 7440360 Antimony 6 9 10 7 3.67 4 L 

66 7440382 Arsenic 7 8 10 8 4.00 4 L 

61 1912249 Atrazine 5 7 9 6 3.50 4 L 

44 1302789 Bentonite (2) 3 1 3 1 1.00 1 NL 

90 100527 Benzaldehyde (3) 4 6 6 7 2.40 2 NL? 

8 71432 Benzene 5 8 8 7 3.67 4 L 

86 95943 Benzene, 1,2,4,5-tetrachloro- (3) 7 6 5 7 3.20 3 L? 

57 82688 Benzene, pentachloronitro- 6 6 9 6 3.33 3 L? 

38 50328 Benzo(a)pyrene 7 8 4 4 3.00 3 L? 

92 271896 Benzofuran (3) 4 6 1 7 1.80 2 NL? 

93 140114 Benzyl acetate (3) 3 3 5 7 1.20 1 NL 

32 7440428 Boron 4 3 10 10 2.50 3 L? 

14 15541454 Bromate 6 8 10 7 3.83 4 L 

2 75274 Bromodichloromethane 5 8 10 8 3.67 4 L 

17 74839 Bromomethane 6 5 6 7 2.83 3 L? 

82 471341 Calcium carbonate 3 6 10 10 2.83 3 L? 

84 120809 Catechol (2) 4 8 10 6 3.33 3 L? 

35 67663 Chloroform 5 4 10 8 2.83 3 L? 

58 1897456 Chlorothanil 5 8 4 4 2.67 3 L? 

40 2921882 Chlorpyrifos 6 3 9 2 1.83 2 NL? 

45 77929 Citric acid (2) 2 3 7 8 1.33 1 NL 

60 7440484 Cobalt 5 3 4 8 2.00 2 NL? 

25 7758987 Copper sulfate 5 3 10 9 3.00 3 L? 

94 2691410 Cyclotetramethylenetetranitramine (3) 4 6 3 5 1.40 1 NL 

36 333415 Diazinon 7 3 1 1 1.17 1 NL 

23 683181 Dibutyltin dichloride 5 5 10 5 2.67 3 L? 

59 60571 Dieldrin 8 8 3 6 3.33 3 L? 

81 124403 Dimethylamine (2) 4 3 10 8 2.33 2 NL? 

62 298044 Disulfoton 7 6 1 1 1.33 1 NL 

83 75003 Ethane, chloro- (2) 3 8 4 7 2.50 3 L? 

69 106934 Ethylene dibromide 7 8 7 4 3.33 3 L? 

6 7705080 Ferric chloride 3 3 10 10 2.17 2 NL? 

65 50000 Formaldehyde 4 6 10 10 3.33 3 L? 

89 110009 Furan (3) 6 6 6 8 3.00 3 L? 

95 98011 Furfural (3) 6 3 6 7 2.40 2 NL? 

27 87683 Hexachlorobutadiene 7 6 4 5 2.83 3 L? 

96 115117 Isobutene (3) 4 4 10 7 2.40 2 NL? 

97 78795 Isoprene (3) 6 5 8 7 3.20 3 L? 

18 7439921 Lead 7 3 9 8 3.33 3 L? 

50 1309428 Magnesium hydroxide 2 3 10 10 1.83 2 NL? 

9 7439965 Manganese 4 5 10 10 3.17 3 L? 

30 57837191 Metalxyl 4 3 9 3 1.50 2 NL? 

67 16752775 Methomyl 5 6 2 7 2.17 2 NL? 

98 598550 Methyl Carbamate (3) 4 8 5 10 3.60 4 L 

C-1 
EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to the CCL August 2009 

Chemical ID INPUT ATTRIBUTE SCORES 

Team Consensus Blinded 

Decisions 

Chemical 

Algorithm 

Number CASRN Contaminant Name Potency Severity Prevalence Magnitude 

List=4 

Mean 

Integer 

Score L/NL 

99 12108133 Methylcyclopentadienylmanganese 7 9 5 8 3.80 4 L 

tricarbonyl(MMT) (3) 

85 98851 Methylphenyl carbinol (3) 3 3 10 8 1.80 2 NL? 

79 51218452 Metolachlor 4 3 6 7 1.83 2 NL? 

41 21087649 Metribuzin 5 3 1 6 1.50 2 NL? 

29 1634044 MTBE 4 5 5 8 2.33 2 NL? 

42 91203 Naphthalene 5 3 7 6 2.00 2 NL? 

56 14797558 Nitrate 3 3 10 10 2.00 2 NL? 

28 14797650 Nitrite 4 3 10 9 2.50 3 L? 

31 98953 Nitrobenzene 6 5 1 10 2.00 2 NL? 

46 1836755 Nitrofen 5 8 1 5 2.17 2 NL? 

68 100754 N-Nitrosopiperidine 7 8 1 1 1.67 2 NL? 

1 109319 Nonanedioic acid, dihexyl ester (2) 2 7 1 1 1.00 1 NL 

70 95487 o-cresol 4 6 1 1 1.00 1 NL 

71 1910425 Paraquat 5 5 1 1 1.00 1 NL 

78 56382 Parathion 5 3 2 3 1.17 1 NL 

15 14797730 Perchlorate 8 7 9 8 4.00 4 L 

100 77098 Phenolphthalein (3) 3 8 1 7 1.80 2 NL? 

13 584087 Potassium carbonate 3 3 10 10 2.00 2 NL? 

3 1610180 Prometon 5 7 1 1 1.17 1 NL 

72 96184 Propane, 1,2,3-trichloro- 7 8 3 6 3.33 3 L? 

88 106490 p-Toluidine (3) 6 8 5 8 3.60 4 L 

73 91225 Quinoline 8 8 7 6 3.83 4 L 

43 7440235 Sodium 4 4 10 10 2.83 3 L? 

7 144558 Sodium bicarbonate 4 9 10 10 3.17 3 L? 

74 96093 Styrene oxide 6 8 1 1 1.17 1 NL 

39 14808798 Sulfate 4 3 10 10 2.50 3 L? 

75 75650 tert-Butanol 3 3 10 8 1.83 2 NL? 

34 127184 Tetrachloroethylene 5 8 9 7 3.67 4 L 

76 78002 Tetraethyl lead 10 6 9 8 4.00 4 L 

77 62566 Thiourea 4 7 7 5 2.67 3 L? 

64 108883 Toluene 4 3 9 7 2.33 2 NL? 

87 78422 Tri(ethylhexyl) phosphate (3) 4 6 3 5 1.80 2 NL? 

102 102716 Triethanolamine (5) 4 8 8 7 3.00 3 L? 

80 76879 Triphenyltin hydroxide 7 3 10 6 2.83 3 L? 

47 1330207 Xylenes 4 3 9 7 2.17 2 NL? 

12 7646857 Zinc chloride 4 3 10 9 2.50 3 L? 

63 Synthetic 4 1 7 7 1.50 2 NL? 

149 Synthetic 3 2 2 2 1.00 1 NL 

150 Synthetic 5 3 1 2 1.00 1 NL 

151 Synthetic 6 1 2 1 1.00 1 NL 

152 Synthetic 2 8 1 1 1.00 1 NL 

153 Synthetic 6 9 1 4 2.33 2 NL? 

154 Synthetic 8 8 8 2 3.50 4 L 

155 Synthetic 2 2 10 9 2.00 2 NL? 

156 Synthetic 4 1 8 1 1.00 1 NL 

157 Synthetic 7 3 8 3 2.00 2 NL? 

158 Synthetic 4 8 8 1 2.33 2 NL? 

159 Synthetic 10 8 8 1 3.50 4 L 

160 Synthetic 1 3 5 9 1.33 1 NL 

161 Synthetic 9 1 1 7 1.67 2 NL? 

162 Synthetic 1 1 1 1 1.00 1 NL 

163 Synthetic 2 6 1 9 1.83 2 NL? 

164 Synthetic 7 8 4 7 3.67 4 L 

165 Synthetic 4 5 10 9 3.17 3 L? 

166 Synthetic 7 3 10 10 3.67 4 L 

167 Synthetic 2 6 7 7 2.17 2 NL? 

168 Synthetic 6 6 7 5 2.67 3 L? 

169 Synthetic 10 9 10 7 4.00 4 L 

170 Synthetic 5 1 5 3 1.00 1 NL 

171 Synthetic 9 3 3 3 2.00 2 NL? 

172 Synthetic 3 6 1 3 1.00 1 NL 

173 Synthetic 8 7 1 2 2.33 2 NL? 

174 Synthetic 10 5 1 1 2.00 2 NL? 

175 Synthetic 8 8 8 8 4.00 4 L 

176 Synthetic 1 5 10 3 1.33 1 NL 

177 Synthetic 10 4 8 5 3.33 3 L? 

178 Synthetic 3 8 10 1 1.83 2 NL? 

179 Synthetic 10 8 9 4 3.83 4 L 

C-2 
EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to the CCL August 2009 

Chemical ID INPUT ATTRIBUTE SCORES 

Team Consensus Blinded 

Decisions 

Chemical 

Algorithm 

Number CASRN Contaminant Name Potency Severity Prevalence Magnitude 

List=4 

Mean 

Integer 

Score L/NL 

180 Synthetic 5 4 3 8 2.50 3 L? 

181 Synthetic 2 2 10 7 1.17 1 NL 

182 Synthetic 8 5 3 8 3.33 3 L? 

183 Synthetic 1 8 5 10 2.83 3 L? 

184 Synthetic 6 8 2 6 3.00 3 L? 

185 Synthetic 1 2 9 8 1.33 1 NL 

186 Synthetic 6 5 6 6 2.83 3 L? 

187 Synthetic 8 8 8 6 3.83 4 L 

188 Synthetic 1 8 7 7 2.17 2 NL? 

189 Synthetic 9 8 7 10 4.00 4 L 

190 Synthetic 5 4 3 3 1.17 1 NL 

191 Synthetic 10 4 5 3 2.67 3 L? 

192 Synthetic 5 6 3 5 2.17 2 NL? 

193 Synthetic 9 8 9 9 4.00 4 L 

194 Synthetic 6 6 1 1 1.17 1 NL 

195 Synthetic 2 1 8 3 1.17 1 NL 

196 Synthetic 6 2 8 2 1.50 2 NL? 

197 Synthetic 3 7 6 3 2.17 2 NL? 

198 Synthetic 6 8 8 1 2.83 3 L? 

199 Synthetic 9 8 2 3 3.17 3 L? 

200 Synthetic 3 1 1 6 1.17 1 NL 

201 Synthetic 8 2 2 7 2.33 2 NL? 

202 Synthetic 4 9 5 7 3.33 3 L? 

203 Synthetic 8 8 1 7 3.50 4 L 

204 Synthetic 3 5 6 8 2.33 2 NL? 

205 Synthetic 9 8 9 4 3.67 4 L 

206 Synthetic 10 2 1 1 1.50 2 NL? 

207 Synthetic 8 4 6 9 3.33 3 L? 

208 Synthetic 4 8 8 7 3.67 4 L 

209 Synthetic 7 6 9 8 3.83 4 L 

210 Synthetic 4 4 1 4 1.00 1 NL 

211 Synthetic 9 3 4 3 2.33 2 NL? 

212 Synthetic 7 7 7 7 3.50 4 L 

213 Synthetic 4 7 3 5 2.17 2 NL? 

214 Synthetic 7 8 1 4 2.67 3 L? 

215 Synthetic 1 4 10 2 1.33 1 NL 

216 Synthetic 6 3 10 5 2.50 3 L? 

217 Synthetic 2 8 9 3 2.17 2 NL? 

218 Synthetic 2 3 10 5 1.17 1 NL 

219 Synthetic 8 8 6 5 3.67 4 L 

220 Synthetic 5 3 3 10 2.50 3 L? 

221 Synthetic 10 3 2 6 2.83 3 L? 

222 Synthetic 5 8 2 6 2.67 3 L? 

223 Synthetic 8 7 2 7 3.33 3 L? 

224 Synthetic 7 7 9 4 3.67 4 L 

225 Synthetic 2 4 6 9 2.33 2 NL? 

226 Synthetic 8 3 10 6 3.50 4 L 

227 Synthetic 1 8 7 8 2.33 2 NL? 

228 Synthetic 10 8 6 8 3.83 4 L 

229 Synthetic 4 4 4 2 1.00 1 NL 

230 Synthetic 10 8 2 9 3.67 4 L 

231 Synthetic 6 4 5 3 2.00 2 NL? 

232 Synthetic 1 6 5 1 1.00 1 NL 

233 Synthetic 7 6 4 2 2.00 2 NL? 

234 Synthetic 2 3 10 4 1.00 1 NL 

235 Synthetic 8 2 7 2 1.50 2 NL? 

236 Synthetic 6 9 6 6 3.67 4 L 

237 Synthetic 2 8 7 4 2.00 2 NL? 

238 Synthetic 8 8 8 4 3.83 4 L 

239 Synthetic 5 4 1 6 2.33 2 NL? 

240 Synthetic 8 1 1 7 1.67 2 NL? 

241 Synthetic 4 7 4 7 2.83 3 L? 

242 Synthetic 10 8 10 3 3.83 4 L 

243 Synthetic 9 7 4 6 3.83 4 L 

244 Synthetic 4 1 8 9 2.00 2 NL? 

245 Synthetic 7 2 10 6 2.67 3 L? 

246 Synthetic 3 8 10 9 3.67 4 L 

247 Synthetic 9 8 7 8 4.00 4 L 

248 Synthetic 1 1 10 5 1.17 1 NL 

C-3 
 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008

 Classification of the PCCL to CCL August 2009 

APPENDIX D. SOFTWARE SOURCES 

Artificial Neural Networks – ANN methods packaged in R software libraries “MASS” 

and “nnet” are available at no charge from the website http://www.r-project.org, under 

the Free Software Foundation’s GNU General Public License. 

Univariate Decision Tree – CART – methods packaged in the R software library “rpart” 

are available at no charge from the website http://www.r-project.org, under the Free 

Software Foundation’s GNU General Public License. 

Multivariate Decision Tree – QUEST software is available at no charge from the website 

http://www.stat.wisc.edu/~loh/quest.html 

Linear Modeling - Likelihood function was maximized using MathCAD’s built-in 

Maximize function (www.mathsoft.com). 

Multivariate Adaptive Regression Splines – MARS methods packaged in the R software 

library “polspline” are available at no charge from the website http://www.r-project.org, 

under the Free Software Foundation’s GNU General Public License. 

D - 1 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

APPENDIX E. SOLUTIONS TO THE CLASSIFICATION MODELS USED 

IN THE CCL PROCESS 

Artificial Neural Network – The software used does not reveal its decision rule. Instead, it 

provides classifications for contaminants that have been scored for the four attributes. When 

given a complete set of all possible combinations of integer attribute scores, the software 

provides classifications. Although not expressed mathematically, this complete description of 

the decision rule can be seen in Exhibit 4-4. 

Example: Contaminant with scores (3, 4, 5, 6). Exhibit 29 shows this as a dark blue point. Not 

List. 

Simple Linear Model – The maximum likelihood linear model is shown below. Y[i] is the 

estimated team-average classification and Pot[i], Sev[i], Prev[i], Mag[i] are the attribute scores 

for contaminant i. If Y[i] is less than 1.5, then the classification is Not List. Similarly, if Y[i] is 

at least 3.5, then the classification is List. 

Y[i] = -1.671 + 0.241 * Pot[i] + 0.217 * Sev[i] + 0.116 * Prev[i] + 0.170 * Mag[i] 

Example: Contaminant with scores (3, 4, 5, 6). 

Y = -1.671 + 0.241 * 3 + 0.217 * 4 + 0.116 * 5 + 0.170 * 6 = 1.520  Not List 

Multivariate Tree (QUEST) – The solution involves a number of intermediate nodes and 

terminal nodes arranged as shown in Exhibit 4.1.1. When a contaminant encounters an 

intermediate node, a weighted sum of attribute scores is compared to a threshold value. The 

direction the contaminant moves from the node depends on whether the threshold is exceeded. 

Below, vector notation is used below to simplify the description. Letting X[i] be a column 

vector of attribute scores, (Pot[i], Sev[i], Prev[i], Mag[i]), then B1T

*X[i] is the vector product of 

B1 (a column vector of weights) and X[i], which, in turn, is compared with the threshold. When 

the contaminant encounters a terminal node (Node 6, 10, 11, 16, 17, 29, 30, or 31), a 

classification is assigned. 

Node 1: If B1*X[i] < 0.3023, then Node 2, otherwise Node 3. 

Node 2: If B2*X[i] < 0.3844, then Node 4, otherwise Node 5. 

Node 4: If B4*X[i] < 0.6460, then Node 6, otherwise Node 7. 

Node 6: Not List 

Node 7: If B7*X[i] < 3.336, then Node 10, otherwise Node 11. 

Node 10: Not List 

Node 11: Not List? 

Node 5: If B5*X[i] < 1.213, then Node 16, otherwise Node 17. 

Node 16: Not List? 

Node 17: List? 

Node 3: If B3*X[i] < 1.181, then Node 28, otherwise Node 29 

Node 28: If B28*X[i] < 6.460, then Node 30, otherwise Node 31. 

Node 30: List? 

Node 31: List 

E-1
 
 

 

 

 

 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Node 29: List 

Exhibit A.1 – Tree Produced by QUEST (heavy arrows show path of contaminant with attribute 

scores 3, 4, 5, 6) 

Exhibit A.2 - The column vectors of weights: 

B1 B2 B3 B4 B5 B7 B28 

0.01631 

0.01315 

0.007523 

0.01034 

0.03008 

0.02075 

0.01214 

0.02043 

0.05223 

0.06855 

0.03516 

0.01807 

0.06890 

0.01756 

0.01753 

0.05501 

0.07779 

0.06447 

0.03300 

0.04850 

0.3531 

0.1136 

0.07560 

0.2144 

0.2966 

0.3174 

0.1995 

0.1952 

Example: Contaminant with scores X = (3, 4, 5, 6) 

Node 1: B1 T

*X = 0.01631*3 + 0.01315*4 + 0.007523*5 + 0.01034*6 = 0.2012 

This is less than 0.3023, so go to Node 2. 

Node 2: B2 T

*X = 0.03008*3 + 0.02075*4 + 0.01214*5 + 0.02043*6 = 0.3565 

This is less than 0.3844, so go to Node 4. 

E-2
 
 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Node 4: B4T*X = 0.06890*3 + 0.01756*4 + 0.01753*5 + 0.05501*6 = 0.6947 

This exceeds 0.6460, so go to Node 7. 

Node 7: B7T*X = 0.3531*3 + 0.1136*4 + 0.07560*5 + 0.2144*6 = 3.1781 

This is less than 3.336, so go to Node 10. 

Node 10: Not List 

E-3
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Appendix F. Chemicals Reviewed by the EPA Evaluation Team: Summary of Results 

Set 1 Summary Direction - disagree Overall Confidence POTENCY Data Element PREVALENCE Data Element 

CASRN Common Name Model 

Decision 

# 

Evaluators 

% 

agreement 

+/- (+ 

toward 

L) 

Value 

(L=4; 

NL=1) 

Category H% M% L% Value 

H=3; L=1) 

Element 

(L4G) Source 

Type 

(NCAR / 

CAR) 

Element (L4G) Source 

51285 2,4-Dinitrophenol NL 18 100 +/-0 1.00 NL 65% 35% 0% 2.647 Reference 

Dose (RfD) 

IRIS NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

UCMR 

60571 Dieldrin L? - L 18 83 +7 3.66 L 41% 47% 12% 2.294 Lifetime 

Cancer Risk 

(10^-4) 

IRIS CAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR1 

2 

62737 Dichlorvos NL - NL? 16 88 -1 1.63 NL? 33% 53% 13% 2.200 Lifetime 

Cancer Risk 

(10^-4) 

IRIS CAR Percentage of Samples 

(Detects), Surface Water, 

Ambient 

NREC 

63252 Carbaryl NL? 16 69 +/-0 2.00 NL? 33% 47% 20% 2.133 Reference 

Dose (RfD) 

OPP NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR1 

2 

67641 Acetone L? 18 78 -2 2.75 L? 40% 60% 0% 2.400 Reference 

Dose (RfD) 

IRIS NCAR Percentage of Sites 

(Detects), All Water, Ambient 

NAWQA 

67721 Hexachloroethane NL 17 100 +1 1.06 NL 44% 56% 0% 2.438 Reference 

Dose (RfD) 

IRIS NCAR Percentage of Sites 

(Detects), All Water, Ambient 

NAWQA 

72559 p,p'-DDE NL - NL? 16 88 +1 1.61 NL? 40% 53% 7% 2.333 Lifetime 

Cancer Risk 

(10^-4) 

IRIS CAR Percentage of PWSs 

(Detects), All Water, 

Finished 

UCMR 

74839 Methyl bromide 

Bromomethane 

L? 17 82 +3 3.11 L? 47% 47% 7% 2.400 Reference 

Dose (RfD) 

IRIS NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR1 

2 

74873 Chloromethane 

(Methyl chloride) 

L? 16 81 +/-0 2.88 L? 50% 50% 0% 2.500 Reference 

Dose (RfD) 

EPAHA NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR1 

2 

74953 Dibromomethane NL? 14 71 -1 1.92 NL? 36% 57% 7% 2.286 Reference 

Dose (RfD) 

RAISH 

E 

NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR1 

2 

74975 Halon 1011 

(bromochloromethan 

e) 

NL? 13 62 -2 1.83 NL? 40% 60% 0% 2.400 Reference 

Dose (RfD) 

EPAHA NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR1 

2 

75150 Carbon disulfide NL? - L? 15 53 -2 2.21 NL? 29% 64% 7% 2.214 Reference 

Dose (RfD) 

IRIS NCAR Percentage of Sites 

(Detects), All Water, Ambient 

NAWQA 

75343 1,1-Dichloroethane L? 12 67 +1 3.00 L? 18% 64% 18% 2.000 Slope 

Factor 

(Oral) 

OEHHA CAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR1 

2 

75694 CFC-11. 

Trichlorofluoromethan 

e 

L? - L 13 69 +1 3.38 L? 42% 50% 8% 2.333 Reference 

Dose (RfD) 

IRIS NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR1 

2 

75718 CFC-12. 

Dichlorofluoromethan 

e 

NL? 13 77 -4 1.71 NL? 50% 42% 8% 2.417 Reference 

Dose (RfD) 

IRIS NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR1 

2 

F-1 
 

 

 

 

 

 

 

 

 

 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Appendix F. Chemicals Reviewed by the EPA Evaluation Team: Summary of Results 

Set 1 Summary Direction - disagree Overall Confidence POTENCY Data Element PREVALENCE Data Element 

CASRN Common Name Model 

Decision 

# 

Evaluators 

% 

agreement 

+/- (+ 

toward 

L) 

Value 

(L=4; 

NL=1) 

Category H% M% L% Value 

H=3; L=1) 

Element 

(L4G) Source 

Type 

(NCAR / 

CAR) 

Element (L4G) Source 

79345 1,1,2,2-

Tetrachloroethane 

NL? 14 64 +1 2.04 NL? 36% 55% 9% 2.273 Reference 

Dose (RfD) 

EPAHA NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR1 

2 

80626 Methyl methacrylate NL 13 100 +/-0 1.00 NL 58% 42% 0% 2.583 Reference 

Dose (RfD) 

IRIS NCAR Percentage of Sites 

(Detects), All Water, Ambient 

NAWQA 

86500 Azinphos-methyl NL? 12 100 +1 2.15 NL? 27% 64% 9% 2.182 Reference 

Dose (RfD) 

OPP NCAR Percentage of Sites 

(Detects), All Water, Ambient 

NAWQA 

87616 1,2,3-

Trichlorobenzene 

NL - NL? 13 77 -3 1.47 NL 42% 42% 17% 2.250 Lowest 

Observed 

Adverse 

Effect Level 

(LOAEL) 

RTECS NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR1 

2 

87683 Hexachlorobutadiene L? 13 77 -2 2.75 L? 46% 46% 8% 2.385 Reference 

Dose (RfD) 

EPAHA NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR1 

2 

88062 2,4,6-Trichlorophenol NL 13 92 +1 1.08 NL 42% 50% 8% 2.333 Reference 

Dose (RfD) 

EPAHA NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

UCMR 

91203 Naphthalene NL? 13 85 -1 1.93 NL? 67% 33% 0% 2.667 Reference 

Dose (RfD) 

IRIS NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR1 

2 

94746 MCPA NL? - L? 14 71 -1 2.38 NL? 33% 42% 25% 2.083 Reference 

Dose (RfD) 

OPP NCAR Percentage of Sites 

(Detects), All Water, Ambient 

NAWQA 

95498 o-Chlorotoluene NL? 13 77 -4 1.71 NL? 58% 42% 0% 2.583 Reference 

Dose (RfD) 

IRIS NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR1 

2 

95636 1,2,4-

Trimethylbenzene 

NL? 13 69 -1 1.92 NL? 33% 33% 33% 2.000 Reference 

Dose (RfD) 

RAISH 

E 

NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR1 

2 

F-2 
 

 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Appendix F. Chemicals Reviewed by the EPA Evaluation Team: Summary of Results 

Set 2 Summary Direction - disagree Overall Confidence POTENCY Data Element PREVALENCE Data Element 

CASRN Common Name Model 

Decision 

# 

Evaluator 

s 

% 

agreement 

+/- (+ 

toward 

L) 

Value 

(L=4; 

NL=1) 

Categor 

y 

H% M% L% Value 

H=3; L=1) 

Element 

(L4G) Source 

Type 

(NCAR 

/ CAR) 

Element (L4G) Source 

2212671 Molinate L? 19 84 -1 3 L? 32% 58% 11% 2.211 Reference 

Dose (RfD) 

IRIS NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

UCMR 

2312358 Propargite L? 18 72 -5 3 L? 24% 59% 18% 2.059 Reference 

Dose (RfD) 

OPP NCAR Percentage of Sites 

(Detects), All Water, 

Ambient 

NAWQA 

5989275 (D)-Limonene NL? 17 82 -4 2 NL? 24% 59% 18% 2.059 No 

Observed 

Effect Level 

(NOEL) 

NTP NCAR Percentage of 

Samples (Detects), 

Surface Water, 

Ambient 

NREC 

7439987 Molybdenum L? - L 18 78 +/-0 3 L? 50% 39% 11% 2.389 UL IOM NCAR Percentage of 

Samples (Detects), 

All Water, Finished 

NIRS 

7440020 Nickel L? 18 89 -2 3 L? 28% 67% 6% 2.222 Reference 

Dose (RfD) 

IRIS NCAR Percentage of 

Samples (Detects), 

All Water, Finished 

NIRS 

7440097 Potassium L? 18 44 -9 2 NL? 24% 24% 53% 1.706 Lowest 

Observed 

Adverse 

Effect Level 

(LOAEL) 

NAS NCAR Percentage of 

Samples (Detects), 

All Water, Finished 

NIRS 

7440213 Silicon L 18 61 -4 3 L? 17% 33% 50% 1.667 Lethal Dose 

50 (LD50) 

RTECS NCAR Percentage of 

Samples (Detects), 

All Water, Finished 

NIRS 

7440235 Sodium L? 19 68 -3 3 L? 26% 37% 37% 1.895 Lowest 

Observed 

Adverse 

Effect Level 

(LOAEL) 

RTECS NCAR Percentage of 

Samples (Detects), 

All Water, Finished 

NIRS 

7440246 Strontium L? 19 74 +/-0 3 L? 26% 47% 26% 2.000 Reference 

Dose (RfD) 

IRIS NCAR Percentage of 

Samples (Detects), 

All Water, Finished 

NIRS 

7440428 Boron L? 18 61 +3 3 L? 24% 53% 24% 2.000 Reference 

Dose (RfD) 

IRIS NCAR Percentage of 

Samples (Detects), 

All Water, Finished 

NIRS 

7440484 Cobalt NL? - L? 17 71 -1 2 NL? 24% 53% 24% 2.000 MRL-Int ATSDR NCAR Percentage of 

Samples (Detects), 

All Water, Finished 

NIRS 

7440564 Germanium L? 18 61 -2 3 L? 18% 24% 59% 1.588 Lowest 

Observed 

Adverse 

Effect Level 

(LOAEL) 

RTECS NCAR Percentage of 

Samples (Detects), 

All Water, Finished 

NIRS 

F-3 
 

 

 

 

 

 

 

 

 

 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Appendix F. Chemicals Reviewed by the EPA Evaluation Team: Summary of Results 

Set 2 Summary Direction - disagree Overall Confidence POTENCY Data Element PREVALENCE Data Element 

CASRN Common Name Model 

Decision 

# 

Evaluator 

s 

% 

agreement 

+/- (+ 

toward 

L) 

Value 

(L=4; 

NL=1) 

Categor 

y 

H% M% L% Value 

H=3; L=1) 

Element 

(L4G) Source 

Type 

(NCAR 

/ CAR) 

Element (L4G) Source 

7440622 Vanadium L? - L 18 78 -4 3 L? 18% 59% 24% 1.941 MRL-Int ATSDR NCAR Percentage of 

Samples (Detects), 

All Water, Finished 

NIRS 

7664417 Ammonia NL? 17 82 -4 2 NL? 24% 65% 12% 2.118 Reference 

Dose (RfD) 

RAISHE NCAR Percentage of Sites 

(Detects), All Water, 

Ambient 

NAWQA 

7723140 White Phosphorus L 19 100 -1 4 L 63% 32% 5% 2.579 Reference 

Dose (RfD) 

IRIS NCAR Percentage of 

Samples (Detects), 

All Water, Finished 

NIRS 

13071799 Terbufos NL 17 82 +3 1 NL 63% 31% 6% 2.563 Reference 

Dose (RfD) 

OPP NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

UCMR 

13194484 Ethoprop NL? 16 81 +2 2 NL? 33% 39% 28% 2.056 Reference 

Dose (RfD) 

OPP NCAR Percentage of Sites 

(Detects), All Water, 

Ambient 

NAWQA 

13494809 Tellurium NL? - L? 16 56 +2 2 NL? 18% 18% 65% 1.529 NOAEL Journal NCAR Percentage of 

Samples (Detects), 

All Water, Finished 

NIRS 

14797730 Perchlorate NL? - L? 16 50 +6 3 L? 33% 47% 20% 2.133 Reference 

Dose (RfD) 

IRIS NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

UCMR 

16655826 3-Hydroxycarbofuran L? 18 83 +2 3 L? 29% 53% 18% 2.118 RfD OPP NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR12 

16752775 Methomyl NL? 16 56 -1 2 NL? 27% 67% 7% 2.200 Reference 

Dose (RfD) 

OPP NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR12 

21087649 Metribuzin NL - NL? 16 69 +/-0 2 NL? 50% 31% 19% 2.313 Reference 

Dose (RfD) 

OPP NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR12 

21725462 Cyanazine NL? 17 65 +/-0 2 NL? 31% 63% 6% 2.250 Reference 

Dose (RfD) 

EPAHA NCAR Percentage of Sites 

(Detects), All Water, 

Ambient 

NAWQA 

25013165 Butylated hydroxyanisole NL? 15 73 +/-0 2 NL? 13% 40% 47% 1.667 Lowest 

Observed 

Adverse 

Effect Level 

(LOAEL) 

RTECS NCAR Percentage of 

Samples (Detects), 

Surface Water, 

Ambient 

NREC 

25057890 Bentazon NL? 15 53 +1 2 NL? 36% 57% 7% 2.286 Reference 

Dose (RfD) 

IRIS NCAR Percentage of Sites 

(Detects), All Water, 

Ambient 

NAWQA 

27314132 Norflurazon NL? 14 79 +2 2 NL? 31% 46% 23% 2.077 Reference 

Dose (RfD) 

OPP NCAR Percentage of Sites 

(Detects), All Water, 

Ambient 

NAWQA 

F-4 
 

 

 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Appendix F. Chemicals Reviewed by the EPA Evaluation Team: Summary of Results 

Set 2 Summary Direction - disagree Overall Confidence POTENCY Data Element PREVALENCE Data Element 

CASRN Common Name Model 

Decision 

# 

Evaluator 

s 

% 

agreement 

+/- (+ 

toward 

L) 

Value 

(L=4; 

NL=1) 

Categor 

y 

H% M% L% Value 

H=3; L=1) 

Element 

(L4G) Source 

Type 

(NCAR 

/ CAR) 

Element (L4G) Source 

34014181 Tebuthiuron NL - NL? 15 73 -4 1 NL 53% 33% 13% 2.400 Reference 

Dose (RfD) 

OPP NCAR Percentage of Sites 

(Detects), All Water, 

Ambient 

NAWQA 

34256821 Acetochlor NL 16 69 +4 1 NL 67% 20% 13% 2.533 Reference 

Dose (RfD) 

IRIS NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

UCMR 

51218452 Metolachlor NL? 13 69 -3 2 NL? 38% 54% 8% 2.308 Reference 

Dose (RfD) 

OPP NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR12 

F-5 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Appendix F. Chemicals Reviewed by the EPA Evaluation Team: Summary of Results 

Set 3 Summary Direction - disagree Overall Confidence 

POTENCY Data Element PREVALENCE Data Element 

CASRN Common Name Model 

Decision 

# 

Evaluators 

% 

agreement 

+/- (+ 

toward L) 

Value 

(L=4; 

NL=1) 

Category H% M% L% 

Value 

H=3; 

L=1) 

Element 

(L4G) Source 

Type 

(NCAR / 

CAR) 

Element (L4G) Source 

96184 1,2,3-Trichloropropane NL? 16 75 +1 2.12 NL? 44% 31% 25% 2.188 Reference 

Dose (RfD) 

IRIS NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR12 

96333 Methyl acrylate NL 15 93 +1 1.07 NL 40% 53% 7% 2.333 Reference 

Dose (RfD) 

RAISHE NCAR Percentage of Sites 

(Detects), All Water, 

Ambient 

NAWQA 

98066 tert-Butylbenzene NL? 16 75 -1 1.97 NL? 19% 69% 13% 2.063 Lowest 

Observed 

Adverse 

Effect Level 

(LOAEL) 

RTECS NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR12 

98953 Nitrobenzene NL?-L? 16 44 +5 2.75 L? 31% 38% 31% 2.000 Reference 

Dose (RfD) 

IRIS NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

UCMR 

103651 n-Propylbenzene NL? 16 94 +1 2.03 NL? 31% 50% 19% 2.125 Lowest 

Observed 

Adverse 

Effect Level 

(LOAEL) 

RTECS NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR12 

106434 p-Chlorotoluene NL? 15 87 -1 1.94 NL? 31% 56% 13% 2.188 Reference 

Dose (RfD) 

EPAHA 

/ IRIS 

NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR12 

107028 Acrolein L?-L 16 69 +1 3.53 L 25% 63% 13% 2.125 Reference 

Dose (RfD) 

RAISHE NCAR Percentage of Sites 

(Detects), All Water, 

Ambient 

NAWQA 

107131 Acrylonitrile NL?-NL 15 73 +3 1.78 NL? 20% 73% 7% 2.133 Lifetime 

Cancer Risk 

(10^-4) 

EPAHA CAR Percentage of Sites 

(Detects), All Water, 

Ambient 

NAWQA 

108054 Vinyl acetate NL 15 100 +/- 0 1.00 NL 40% 47% 13% 2.267 Reference 

Dose (RfD) 

RAISHE NCAR Percentage of Sites 

(Detects), All Water, 

Ambient 

NAWQA 

108861 Bromobenzene NL? 16 69 +3 2.09 NL? 27% 53% 20% 2.067 Reference 

Dose (RfD) 

RAISHE NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR12 

109999 Tetrahydrofuran L? 16 75 -1 2.93 L? 13% 47% 40% 1.733 No 

Observed 

Effect Level 

(NOEL) 

Journal NCAR Percentage of Sites 

(Detects), All Water, 

Ambient 

NAWQA 

115968 Trichlorethyl phosphate NL?-L? 14 50 -3 2.39 NL? 7% 60% 33% 1.733 Reference 

Dose (RfD) 

RAISHE NCAR Percentage of 

Samples (Detects), 

Surface Water, 

Ambient 

NREC 

121142 2,4-Dinitrotoluene L?-L 15 60 +1 3.53 L 38% 54% 8% 2.308 Lifetime 

Cancer Risk 

(10^-4) 

EPAHA CAR Percentage of PWSs 

(Detects), All Water, 

Finished 

UCMR 

F-6 
 

 

 

 

 

 

 

 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Appendix F. Chemicals Reviewed by the EPA Evaluation Team: Summary of Results 

Set 3 Summary Direction - disagree Overall Confidence 

POTENCY Data Element PREVALENCE Data Element 

CASRN Common Name Model 

Decision 

# 

Evaluators 

% 

agreement 

+/- (+ 

toward L) 

Value 

(L=4; 

NL=1) 

Category H% M% L% 

Value 

H=3; 

L=1) 

Element 

(L4G) Source 

Type 

(NCAR / 

CAR) 

Element (L4G) Source 

121755 Malathion NL 13 77 +3 1.23 NL 31% 54% 15% 2.154 Reference 

Dose (RfD) 

OPP NCAR Percentage of Sites 

(Detects), All Water, 

Ambient 

NAWQA 

122667 1,2-Diphenylhydrazine NL-NL? 12 100 +/-0 1.50 NL? 64% 27% 9% 2.545 Lifetime 

Cancer Risk 

(10^-4) 

IRIS CAR Percentage of PWSs 

(Detects), All Water, 

Finished 

UCMR 

126987 Methacrylonitrile NL 14 93 +1 1.11 NL 54% 31% 15% 2.385 Reference 

Dose (RfD) 

IRIS NCAR Percentage of Sites 

(Detects), All Water, 

Ambient 

NAWQA 

135988 sec-Butylbenzene NL? 15 93 +/-0 2.00 NL? 29% 64% 7% 2.214 Lowest 

Observed 

Adverse 

Effect Level 

(LOAEL) 

RTECS NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR12 

298044 Disulfoton NL 14 71 +3 1.35 NL 38% 38% 23% 2.154 Reference 

Dose (RfD) 

OPP, 

2002 

NCAR Percentage of PWSs 

(Detects), All Water, 

Finished 

UCMR 

309002 Aldrin L? 15 73 +4 3.27 L? 33% 47% 20% 2.133 Lifetime 

Cancer Risk 

(10^-4) 

EPAHA CAR Percentage of PWSs 

(Detects), All Water, 

Finished 

NCODR12 

314409 Bromacil NL? 15 73 -4 1.80 NL? 36% 43% 21% 2.143 Reference 

Dose (RfD) 

OPP NCAR Percentage of Sites 

(Detects), All Water, 

Ambient 

NAWQA 

50000 Formaldehyde L?-L 15 67 -3 3.27 L? 13% 47% 40% 1.733 Reference 

Dose (RfD) 

IRIS NCAR Release, Number of 

States 

TRI 

50997 D-Glucose NL?-NL 14 64 -3 2.14 NL? 8% 8% 85% 1.231 Lethal Dose 

50 (LD50) 

RTECS NCAR Production Volume CUS/IUR 

75570 Tetramethylammonium 

chloride 

L? 14 57 -3 2.77 L? 14% 7% 79% 1.357 Lethal Dose 

50 (LD50) 

RTECS NCAR Production Volume CUS/IUR 

78002 Tetraethyl lead L 15 73 -2 3.88 L 7% 43% 50% 1.571 Reference 

Dose (RfD) 

IRIS NCAR Production Volume CUS/IUR 

78795 Isoprene L?-L 15 47 -7 2.94 L? 7% 21% 71% 1.357 Lowest 

Observed 

Adverse 

Effect Level 

(LOAEL) 

RTECS NCAR Production Volume CUS/IUR 

78820 Isobutyronitrile L?-L 15 33 -7 3.00 L? 7% 0% 93% 1.133 Lethal Dose 

50 (LD50) 

HSDB NCAR Production Volume CUS/IUR 

101779 Benzenamine, 4,4'-

methylenebisL 15 67 -5 3.40 L? 13% 47% 40% 1.733 Slope Factor 

(Oral) 

OEHHA CAR Release, Number of 

States 

TRI 

F-7 
 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Appendix F. Chemicals Reviewed by the EPA Evaluation Team: Summary of Results 

Set 3 Summary Direction - disagree Overall Confidence 

POTENCY Data Element PREVALENCE Data Element 

CASRN Common Name Model 

Decision 

# 

Evaluators 

% 

agreement 

+/- (+ 

toward L) 

Value 

(L=4; 

NL=1) 

Category H% M% L% 

Value 

H=3; 

L=1) 

Element 

(L4G) Source 

Type 

(NCAR / 

CAR) 

Element (L4G) Source 

108930 Cyclohexanol L?-L 14 64 -6 2.83 L? 7% 21% 71% 1.357 Lethal Dose 

50 (LD50) 

RTECS NCAR Release, Number of 

States 

TRI 

302012 Hydrazine L 15 87 -1 3.79 L 13% 53% 33% 1.800 Lifetime 

Cancer Risk 

(10^-4) 

IRIS CAR Release, Number of 

States 

TRI 

625558 Isopropyl formate L 13 54 -5 3.46 L? 0% 7% 93% 1.071 Lethal Dose 

50 (LD50) 

RTECS NCAR Production Volume CUS/IUR 

1111780 Ammonium carbamate L? 13 77 -2 2.75 L? 14% 7% 79% 1.357 Lethal Dose 

50 (LD50) 

RTECS NCAR Production Volume CUS/IUR 

1335326 Lead acetate L 14 50 -6 3.35 L? 8% 17% 75% 1.333 Slope 

Factor 

(Oral) 

OEHHA CAR Production Volume CUS/IUR 

3268493 Methional L? 13 69 -2 2.86 L? 0% 31% 69% 1.308 Lethal Dose 

50 (LD50) 

RTECS NCAR Production Volume CUS/IUR 

4719044 Hexahydro-1,3,5-tris(2-

hydroxyethyl)-s-triazine 

L 13 38 -6 3.41 L? 7% 7% 86% 1.214 Lethal Dose 

50 (LD50) 

RTECS NCAR Production Volume CUS/IUR 

5216251 4-Chlorobenzotrichloride L?-L 14 86 -2 3.21 L? 14% 36% 50% 1.643 NOAEL OPPT NCAR Production Volume CUS/IUR 

6610293 Methylthiosemicarbazide L? 14 71 -2 2.75 L? 0% 15% 85% 1.154 Lethal Dose 

50 (LD50) 

RTECS NCAR Production Volume CUS/IUR 

13463406 Iron pentacarbonyl L?-L 13 62 -6 2.81 L? 0% 31% 69% 1.308 Lethal Dose 

50 (LD50) 

HSDB NCAR Release, Number of 

States 

TRI 

23422539 Methanimidamide, N,Ndimethyl-N'-[3-

[[(methylamino)carbonyl]o 

xy]phenyl]-, 

monohydrochloride 

L?-L 14 57 -3 3.27 L? 17% 42% 42% 1.750 Reference 

Dose (RfD) 

OPP NCAR Release, Number of 

States 

NCFAP 

71751412 Avermectin B1 L?-L 13 69 -1 3.39 L? 14% 29% 57% 1.571 ADI JMPR 

1997 

NCAR Release, Number of 

States 

NCFAP 

91465086 Cyclopropanecarboxylic 

acid, 3-2-chloro-3,3,3-

trifluoro-1-propenyl)-2,2-

dimethyl- cyano(3-

phenoxyphenyl)methyl 

ester, 

1.alpha.(S*),3.alpha.(Z)-

(.+ -.)-

L?-L 14 71 -4 3.11 L? 14% 36% 50% 1.643 Reference 

Dose (RfD) 

IRIS NCAR Release, Number of 

States 

NCFAP 

F-8 
 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Appendix F. Chemicals Reviewed by the EPA Evaluation Team: Summary of Results 

Set 4 Summary Direction - disagree Overall Confidence POTENCY Data Element PREVALENCE Data 

Element 

CASRN Common Name Model 

Decision 

# 

Evaluators 

% 

agreement 

+/- (+ 

toward L) 

Value 

(L=4; 

NL=1) 

Category H% M% L% 

Value 

H=3; 

L=1) 

Element (L4G) Source 

Type 

(NCAR / 

CAR) 

Element 

(L4G) Source 

Cobalt compounds L 8 75 -2 3.81 L 22% 22% 56% 1.667 Lowest Observed 

Adverse Effect 

Level (LOAEL) 

Journal NCAR Release, 

Number of 

States 

TRI 

51796 Urethane L 8 63 -2 3.79 L 22% 0% 78% 1.444 No Observed 

Effect Level 

(NOEL) 

Journal NCAR Release, 

Number of 

States 

TRI 

55630 Nitroglycerin L? - L 9 78 +/- 0 3.50 L 9% 18% 73% 1.364 Lowest Observed 

Adverse Effect 

Level (LOAEL) 

RTECS NCAR Release, 

Number of 

States 

TRI 

60355 Acetamide L 9 67 -2 3.56 L 20% 20% 60% 1.600 Slope Factor 

(Oral) 

OEHHA CAR Release, 

Number of 

States 

TRI 

62533 Aniline L? - L 10 70 +2 3.61 L 20% 30% 50% 1.700 Reference Dose 

(RfD) 

RAISHE NCAR Release, 

Number of 

States 

TRI 

67561 Methanol L? - L 10 60 +1 3.45 L? 17% 50% 33% 1.833 Reference Dose 

(RfD) 

IRIS NCAR Release, 

Number of 

States 

TRI 

71363 1-Butanol L? - L 11 55 -1 3.33 L? 17% 33% 50% 1.667 Reference Dose 

(RfD) 

IRIS NCAR Release, 

Number of 

States 

TRI 

75218 Ethylene oxide L 9 78 -2 3.78 L 36% 18% 45% 1.909 Slope Factor 

(Oral) 

OEHHA CAR Release, 

Number of 

States 

TRI 

75569 Propylene oxide L 9 89 -1 3.78 L 36% 18% 45% 1.909 Slope Factor 

(Oral) 

OPP CAR Release, 

Number of 

States 

TRI 

76879 Triphenyltin hydroxide L 10 80 -2 3.90 L 22% 44% 33% 1.889 Slope Factor 

(Oral) 

OPP CAR Release, 

Number of 

States 

NCFAP 

80159 Cumene hydroperoxide L 10 60 -3 3.61 L 18% 9% 73% 1.455 Lowest Observed 

Adverse Effect 

Level (LOAEL) 

RTECS NCAR Release, 

Number of 

States 

TRI 

106990 1,3-Butadiene L 11 73 -2 3.80 L 27% 9% 64% 1.636 Slope Factor 

(Oral) 

OEHHA CAR Release, 

Number of 

States 

TRI 

107211 Ethylene glycol L 10 80 -2 3.70 L 27% 36% 36% 1.909 Reference Dose 

(RfD) 

IRIS NCAR Release, 

Number of 

States 

TRI 

109864 2-Methoxyethanol L 9 78 -3 3.65 L 30% 10% 60% 1.700 Reference Dose 

(RfD) 

RAISHE NCAR Release, 

Number of 

States 

TRI 

121448 Triethylamine L 7 43 -4 3.36 L? 0% 29% 71% 1.286 Lowest Observed 

Adverse Effect 

Level (LOAEL) 

RTECS NCAR Release, 

Number of 

States 

TRI 

F-9 
 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Appendix F. Chemicals Reviewed by the EPA Evaluation Team: Summary of Results 

Set 4 Summary Direction - disagree Overall Confidence POTENCY Data Element PREVALENCE Data 

Element 

CASRN Common Name Model 

Decision 

# 

Evaluators 

% 

agreement 

+/- (+ 

toward L) 

Value 

(L=4; 

NL=1) 

Category H% M% L% 

Value 

H=3; 

L=1) 

Element (L4G) Source 

Type 

(NCAR / 

CAR) 

Element 

(L4G) Source 

123911 1,4-Dioxane L 9 100 +/-0 4.00 L 30% 30% 40% 1.900 Lifetime Cancer 

Risk (10^-4) 

EPAHA CAR Release, 

Number of 

States 

TRI 

133062 Captan L 10 70 -2 3.72 L 33% 22% 44% 1.889 Slope Factor 

(Oral) 

OPP CAR Release, 

Number of 

States 

NCFAP 

137304 Ziram L 8 88 -1 3.75 L 13% 25% 63% 1.500 Slope Factor 

(Oral) 

OPP CAR Release, 

Number of 

States 

NCFAP 

319846 .alpha.-

Hexachlorocyclohexane 

L? 12 67 +1 3.00 L? 18% 64% 18% 2.000 Lifetime Cancer 

Risk (10^-4) 

IRIS CAR Percentage 

of Sites 

(Detects), All 

Water, 

Ambient 

NAWQA 

330541 Diuron NL? 13 77 +3 2.19 NL? 18% 64% 18% 2.000 Reference Dose 

(RfD) 

OPP NCAR Percentage 

of PWSs 

(Detects), All 

Water, 

Finished 

UCMR 

330552 Linuron NL 12 92 +/-0 1.00 NL 50% 40% 10% 2.400 Reference Dose 

(RfD) 

OPP NCAR Percentage 

of PWSs 

(Detects), All 

Water, 

Finished 

UCMR 

333415 Diazinon NL 11 91 +1 1.09 NL 45% 36% 18% 2.273 Reference Dose 

(RfD) 

OPP NCAR Percentage 

of PWSs 

(Detects), All 

Water, 

Finished 

UCMR 

541731 m-Dichlorobenzene NL? 13 77 +1 2.00 NL? 45% 45% 9% 2.364 Reference Dose 

(RfD) 

EPAHA NCAR Percentage 

of PWSs 

(Detects), All 

Water, 

Finished 

NCODR12 

542756 Telone L? 13 62 +3 3.23 L? 25% 50% 25% 2.000 Slope Factor 

(Oral) 

OPP CAR Percentage 

of PWSs 

(Detects), All 

Water, 

Finished 

NCODR12 

630206 1,1,1,2-Tetrachloroethane L? 13 77 -1 2.88 L? 27% 64% 9% 2.182 Lifetime Cancer 

Risk (10^-4) 

EPAHA CAR Percentage 

of PWSs 

(Detects), All 

Water, 

Finished 

NCODR12 

F-10 
 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Appendix F. Chemicals Reviewed by the EPA Evaluation Team: Summary of Results 

Set 4 Summary Direction - disagree Overall Confidence POTENCY Data Element PREVALENCE Data 

Element 

CASRN Common Name Model 

Decision 

# 

Evaluators 

% 

agreement 

+/- (+ 

toward L) 

Value 

(L=4; 

NL=1) 

Category H% M% L% 

Value 

H=3; 

L=1) 

Element (L4G) Source 

Type 

(NCAR / 

CAR) 

Element 

(L4G) Source 

759944 S-Ethyl 

dipropylthiocarbamate 

NL 12 75 +3 1.38 NL 55% 45% 0% 2.545 Reference Dose 

(RfD) 

IRIS NCAR Percentage 

of PWSs 

(Detects), All 

Water, 

Finished 

UCMR 

944229 Fonofos NL 12 83 +/-0 1.00 NL 60% 40% 0% 2.600 Reference Dose 

(RfD) 

IRIS NCAR Percentage 

of PWSs 

(Detects), All 

Water, 

Finished 

UCMR 

1313275 Molybdenum oxide 

(MoO3) 

L 11 45 -3 3.38 L? 0% 25% 75% 1.250 RfD (UL) DRI NCAR Release, 

Number of 

States 

TRI 

1582098 Trifluralin NL - NL? 11 82 +2 1.59 NL? 56% 44% 0% 2.556 Reference Dose 

(RfD) 

OPP NCAR Percentage 

of Sites 

(Detects), All 

Water, 

Ambient 

NAWQA 

1610180 Prometon NL 12 100 +/-0 1.00 NL 40% 40% 20% 2.200 Reference Dose 

(RfD) 

IRIS NCAR Percentage 

of PWSs 

(Detects), All 

Water, 

Finished 

UCMR 

1634044 Methyl tert-butyl ether L? 12 58 +5 3.42 L? 10% 70% 20% 1.900 Slope Factor 

(Oral) 

OEHHA CAR Percentage 

of PWSs 

(Detects), All 

Water, 

Finished 

UCMR 

1861321 Chlorthal-dimethyl 

(Dacthal) 

NL? 12 67 +4 2.25 NL? 33% 56% 11% 2.222 Reference Dose 

(RfD) 

OPP NCAR Percentage 

of Sites 

(Detects), All 

Water, 

Ambient 

NAWQA 

1897456 Chlorothalonil NL? 12 75 +3 2.17 NL? 20% 60% 20% 2.000 Reference Dose 

(RfD) 

OPP NCAR Percentage 

of Sites 

(Detects), All 

Water, 

Ambient 

NAWQA 

2164172 Fluometuron NL 11 91 +/-0 1.00 NL 20% 70% 10% 2.100 Reference Dose 

(RfD) 

IRIS NCAR Percentage 

of Sites 

(Detects), All 

Water, 

Ambient 

NAWQA 

F-11 
 

 

EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Appendix F. Chemicals Reviewed by the EPA Evaluation Team: Summary of Results 

Set 4 Summary Direction - disagree Overall Confidence POTENCY Data Element PREVALENCE Data 

Element 

CASRN Common Name Model 

Decision 

# 

Evaluators 

% 

agreement 

+/- (+ 

toward L) 

Value 

(L=4; 

NL=1) 

Category H% M% L% 

Value 

H=3; 

L=1) 

Element (L4G) Source 

Type 

(NCAR / 

CAR) 

Element 

(L4G) Source 

26471625 Toluene diisocyanate L 10 80 -1 3.89 L 25% 25% 50% 1.750 Slope Factor 

(Oral) 

OEHHA CAR Release, 

Number of 

States 

TRI 

F-12 
EPA-OGWDW Final CCL 3 Chemicals: EPA 815-R-09-008 

Classification of the PCCL to CCL August 2009 

Appendix G. PCCL Contaminants with Incomplete Data for Scoring or that 

had Parent Compounds Scored in Developing the Draft CCL 3 

ASRN Substance Name Common Name 

30552 Pyrrolidine, 1-nitroso- N-nitrosopyrrolidine (NPYR) 

595956 Ethanamine, N-methyl-N-nitroso- N-Nitrosomethylethylamine (NMEA) 

83181 Stannane, dibutyldichloro- Dibutyltin dichloride 

53731 Stannane, dichlorodimethyl- Dimethyltin dichloride 

18086 Stannane, dibutyloxo- Dibutyltin oxide 

60021 Benzenesulfonic acid, 5-chloro-2-[(2-hydroxy-1-

naphthalenyl)azo]-4-methyl-, barium salt (2:1) C.I. Pigment Red 53, barium salt (2:1) 

47418 Lithium chloride (LiCl) Lithium chloride 

82992 Sulfurous acid Sulfurous acid 

83064 Hydrogen sulfide (H2S) Hydrogen sulfide 

83188 Thiosulfuric acid (H2S2O3), diammonium salt Ammonium thiosulfate 

108133 Manganese, tricarbonyl[(1,2,3,4,5-.eta.)-1-methyl-2,4-

cyclopentadien-1-yl]- Methylcyclopentadienyl manganese tricarbonyl 

808607 Quartz (SiO2) Quartz (SiO2) 

5003 Ethane, chloro- Chloroethane 

5025 Ethene, fluoro- Vinyl fluoride 

5887 Ethane, 2-chloro-1,1,1-trifluoro- HCFC-133a 

02716 Ethanol, 2,2',2''-nitrilotris- Triethanolamine 

06876 7-Oxabicyclo[4.1.0]heptane, 3-oxiranyl- 1,2-Epoxy-4-(epoxyethyl)cyclohexane

15117 1-Propene, 2-methyl- Isobutene 

16143 Ethene, tetrafluoro- Tetrafluoroethene

27060 2-Propanone, oxime 2-Propanone oxime 

40291 Thorium Thorium-232 

028156 Ozone Ozone 

018527 2-Propanol, 1-(1,1-dimethylethoxy)- Propylene glycol mono-t-butyl ether 

07289 1,3,5-Triazine-2,4-diamine, 6-chloro-N-ethyl- Desisopropylatrazine 

13275 Molybdenum oxide (MoO3) Molybdenum trioxide 

90654 1,3,5-Triazine-2,4-diamine, 6-chloro-N-(1-methylethyl)- Desethylatrazine 

81529 Hypochlorous acid, sodium salt Sodium hypochlorite 

277671 2-Thiophenecarboxylic acid, 3-[[[[(4-methoxy-6-methyl-1,3,5-

triazin-2-yl)amino]carbonyl]amino]sulfonyl]- Thifensulfuron 

578126 Quizalofop Quizalofop 

070156 Terbufos-O-analogue sulfone Terbufos-O-analogue sulfone

 Diazinon oxygen analog Diazinon oxygen analog 

 DCPA mono/di-acid degradate Dacthal mono/di-acid degradate 

G-1 

C

9

10

6

7

8

51

74

77

77

77

12

14

7

7

7

1

1

1

1

1

74

10

57

10

13

61

76

79

76

56